DEVELOPMENT OF IMPROVED CONTROLLER FOR

MPPT BASED SOLAR FED BRUSHLESS DC MOTOR

Shamsun Nahar

DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING

DHAKA UNIVERSITY OF ENGINEERING & TECHNOLOGY, GAZIPUR

January, 2020

ii

DEVELOPMENT OF IMPROVED CONTROLLER FOR

MPPT BASED SOLAR FED BRUSHLESS DC MOTOR

A thesis submitted to the

Department of Electrical and Electronic Engineering (EEE)

of

Dhaka University of Engineering & Technology (DUET), Gazipur

In partial fulfillment of the requirement for the degree of

MASTER OF SCIENCE IN ELECTRICAL AND ELECTRONIC ENGINEERING

by

Shamsun Nahar

Student ID: 142235-P

Under Supervision of

Dr. Md. Raju Ahmed

Professor, Department of Electrical and Electronic Engineering,

Dhaka University of Engineering & Technology (DUET), Gazipur

DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING

DHAKA UNIVERSITY OF ENGINEERING & TECHNOLOGY, GAZIPUR

January, 2020

iii

1

ii

Dedicated

To

My Parents

iii

Acknowledgements

At first, all praise to almighty Allah who has given me the opportunity, strength and

patience to complete the thesis work successfully.

I would like to express my sincere gratitude and profound respect to my supervisor Dr. Md.

Raju Ahmed, Professor, Department of Electrical and Electronic Engineering (EEE),

Dhaka University of Engineering and Technology, (DUET), Gazipur for his encouragement

and endless support throughout the progress of the work. His constant guidance and

research motivation have inspired me during my research work. I am thankful to him for his

valuable suggestions, continuous supervision, kind co-operation, constructive criticisms,

without which this work would not have been completed.

I would also like to express my heartfelt gratitude and thanks to Dr. Md. Arifur Rahman,

Associate Professor, Department of EEE of DUET, Gazipur for his sincere help, support,

kind co-operation, valuable guidance and suggestions in my thesis work.

I am grateful to all the faculty and staffs of the Department of EEE, Dhaka University of

Engineering and Technology (DUET) for their co-operation and continuous support

throughout the thesis work. I am also grateful to those, who have directly or indirectly

helped and encouraged me to complete my thesis.

Finally, I would like to pay my profound gratitude and thanks to my parents and family for

supporting me throughout all my studies and for their help, co-operation, blessings and

continuous inspiration during my thesis work.

iv

Abstract

In recent years, renewable energy systems have received great attention to meet the

increasing global energy problem. Solar PV is one of the promising sources among other

renewable energy sources. But the conversion efficiency of the solar PV system is low. The

power DC-DC converter is an important part in the solar PV system to transfer maximum

power and obtain desired power conversion. To overcome the major disadvantages of

power DC-DC converters such as high voltage and current ripples, low efficiency etc,

interleaving strategies play a significant role. Interleaving strategies in power DC-DC

converters are gaining ever-increasing importance due to its enormous advantages.

Interleaved converter having coupled inductor also helps to minimize the voltage and

current ripples. Besides, BLDC motor is widely used in various applications of solar PV

system because of its many advantages. For achieving desired speed performance from

motor drive system, low ripple input supply and optimum speed controller is necessary.

In this research work, the performance of BLDC motor fed from solar energy using three

phase IBC with coupled inductor is analyzed through MATLAB/Simulink. This converter

circuit is controlled by incremental conductance algorithm. The performance such as stator

current, speed, torque etc of BLDC motor using three phase IBC and three phase IBC with

coupled inductor is analyzed and compared the results by using MATLAB/Simulink. A

comparative analysis among the boost converter, two phase IBC, three phase IBC and three

phase IBC using coupled inductor are presented in term of voltage and current ripples. In

this research, the optimal tuning algorithm particle swarm optimization (PSO) based PID

controller is developed in the system to enhance the speed response of BLDC motor such as

overshoot, settling time, rise time etc. Different controllers like PI, PD and PID controller

are also employed. A performance comparison is made among the speed responses of

BLDC motor using PSO based PID controller, PID controller and without using any

controller.

v

Abbreviations

BLDC Brushless Direct Current

BJT Bipolar Junction Transistor

DC Direct Current

EMF Electromotive Force

GA Genetic Algorithm

IBC Interleaved Boost Converter

InC Incremental Conductance

IGBT Insulated Gate Bipolar Transistor

MPPT Maximum Power Point Tracking

MATLAB Matrix Laboratory

MOSFET Metal Oxide Semiconductor Field Effect Transistor

PV Photovoltaic

P&O Perturb And Observe

PWM Pulse Width Modulation

PI Proportional Integral

PD Proportional Derivative

PID Proportional Integral Derivative

PSO Particle Swarm Optimization

RPM Revolutions Per Minute

SPV Solar Photovoltaic

SEPIC Single Ended Primary Inductor Converter

STC Standard Test Conditions

vi

Table of Contents

Page No.

Declaration i

Dedication ii

Acknowledgements iii

Abstract iv

Abbreviations v

Table of Contents vi

List of Figures ix

List of Tables xii

CHAPTER 1 Introduction

1.1 Introduction 1

1.2 Literature Review 2

1.3 Objectives with specific aims 4

1.4 Outline of the thesis 5

CHAPTER 2 Performance Analysis of Solar MPPT Based Three Phase

Interleaved Boost Converter with Coupled Inductor

2.1 Introduction 7

2.2 Three Phase Interleaved Boost Converter with Coupled Inductor 7

2.2.1 Operational principle of Interleaved Boost Converter (IBC) 8

2.2.2 Operational principle of IBC with coupled inductor 9

2.2.3 Design methodology of Interleaved Boost Converter 11

2.3 Solar Cell and Maximum Power Point Tracking (MPPT) 12

2.3.1 Solar cell 13

2.3.2 Incremental Conductance Algorithm 14

2.4 Simulation and Analysis 15

2.4.1 MATLAB/Simulink model of solar MPPT based three phase IBC

with coupled inductor

16

2.4.2 System component 17

2.4.3 Simulation outputs and analysis 19

2.5 Conclusion 28

vii

CHAPTER 3 Performance Analysis of BLDC Motor Using Three Phase

Interleaved Boost Converter with Coupled Inductor

3.1 Introduction 30

3.2 Brushless DC (BLDC) motor 30

3.2.1 Operation principle of brushless DC motor 31

3.2.2 Mathematical model of BLDC motor 33

3.3 Block diagram of solar MPPT based three phase IBC with coupled

inductor for BLDC motor

35

3.4 Simulation and Analysis 36

3.4.1 MATLAB/Simulink model of solar MPPT based three phase IBC

with coupled inductor for BLDC motor

36

3.4.2 System component 37

3.4.3 Simulation outputs and analysis 37

3.5 Conclusion 45

CHAPTER 4 Speed Control of BLDC Motor Using PSO Algorithm

4.1 Introduction 47

4.2 PI controller 47

4.3 PD controller 48

4.4 PID controller 48

4.4.1 Proportional control 49

4.4.2 Integral control 49

4.4.3 Derivative control 50

4.5 Particle swarm optimization (PSO) algorithm 51

4.6 PSO algorithm based PID controller for the system 54

4.7 Block diagram of the system 56

4.8 Simulation and Analysis 56

4.8.1 MATLAB/Simulink model of the system using PSO algorithm

based PID controller

57

4.8.2 Simulation outputs and analysis 58

4.9 Conclusion 64

CHAPTER 5 Conclusion and Future Works

viii

5.1 Conclusion 66

5.2 Future Works 67

References 69

Publications 79

Appendix 80

ix

List of Figures

Fig. No. Figure Title Page

No.

Fig. 2.1 (a) Conventional boost converter and (b) „n‟ phases interleaved boost

converter

8

Fig. 2.2 Inductor Currents and PWM Signals of IBC (Three phase IBC) 9

Fig. 2.3 Circuit diagram of three phase IBC with directly coupled inductor 10

Fig. 2.4 Equivalent model of directly coupled inductors 10

Fig. 2.5 Equivalent Circuit of Solar Cell 13

Fig. 2.6 Power-Voltage Characteristics of PV Module 15

Fig. 2.7 Flowchart of Incremental Conductance (InC) algorithm 16

Fig. 2.8 MATLAB simulation model of solar MPPT based three phase IBC with

coupled inductor

17

Fig. 2.9 MATLAB simulation model of incremental conductance algorithm with

PWM signal generation.

18

Fig. 2.10 (a) IV and (b) PV characteristics curve of solar panel 20

Fig. 2.11 Switching pulses of a) Boost converter, b) Two phase IBC with 180°

phase shift and c) Three phase IBC (uncoupled and coupled inductor)

with 120° phase shift

21

Fig. 2.12 Inductor current waveforms of (a) boost converter (b) two phase IBC (c)

three phase IBC and (d) three phase IBC with coupled inductor

23

Fig. 2.13 Input current ripple waveforms of (a) boost converter (b) two phase IBC

(c) three phase IBC and (d) three phase IBC with coupled inductor

24

Fig. 2.14 Output current ripple waveforms of (a) boost converter (b) two phase

IBC (c) three phase IBC and (d) three phase IBC with coupled inductor

26

Fig. 2.15 Output voltage ripple waveforms of (a) boost converter (b) two phase

IBC (c) three phase IBC and (d) three phase IBC with coupled inductor

27

Fig. 3.1 Transverse section of BLDC motor 31

Fig. 3.2 Circuit diagram of BLDC motor with three phase inverter 32

Fig. 3.3 Back EMFs, phase currents and Hall sensor signals for each phase 32

Fig. 3.4 Block diagram of solar MPPT (incremental conductance algorithm)

based three phase IBC with coupled inductor for BLDC motor

35

x

Fig. 3.5 MATLAB simulation model of solar MPPT (incremental conductance

algorithm) based three phase IBC with coupled inductor for BLDC motor

36

Fig. 3.6 Hall sensor outputs 38

Fig. 3.7 Gate pulse waveforms 37

Fig. 3.8 Speed waveforms of BLDC motor using three phase IBC 40

Fig. 3.9 Speed waveforms of BLDC motor using three phase IBC with coupled

inductor

40

Fig. 3.10 Stator phase current waveforms of BLDC motor using three phase IBC 41

Fig. 3.11 Stator phase current waveforms of BLDC motor using three phase IBC

with coupled inductor

41

Fig. 3.12 Back EMF waveforms of BLDC motor using three phase IBC 42

Fig. 3.13 Back EMF waveforms of BLDC motor using three phase IBC with

coupled inductor

42

Fig. 3.14 (a) Torque waveform and (b) ripples in torque waveform of BLDC motor

using three phase IBC

43

Fig. 3.15 (a) Torque waveform and (b) ripples in torque waveform of BLDC motor

using three phase IBC with coupled inductor

44

Fig. 4.1 Block diagram of PID controller based system 49

Fig 4.2 Step response of a control system 50

Fig. 4.3 Concept of modification of a searching point by PSO algorithm 52

Fig. 4.4 Flowchart of PSO algorithm 53

Fig. 4.5 Flowchart of PID controller tuning by using PSO algorithm for the

system

55

Fig. 4.6 Block diagram of the system 56

Fig. 4.7 MATLAB simulation model of the system using PSO algorithm based

PID controller

57

Fig. 4.8 MATLAB simulation model of PID controller 58

Fig. 4.9 Speed response of BLDC motor without using any controller 59

Fig. 4.10 Speed response of BLDC motor using PI controller 60

Fig. 4.11 Speed response of BLDC motor using PD controller 60

Fig. 4.12 Speed response of BLDC motor using PID controller 61

Fig. 4.13 The values of error signal for 25 iterations 62

Fig. 4.14 Speed response of BLDC motor using PSO algorithm based PID

controller

63

xi

List of Tables

Table No. Table Title Page

No.

Table 2.1 Specification of solar panel (Solarex MSX60) 18

Table 2.2 Parameters of three phase IBC (uncoupled & coupled inductor) 19

Table 2.3 Performance of conventional boost converter, two and three phase

IBC

28

Table 2.4 Comparison between uncoupled and coupled inductor three phase IBC 28

Table 3.1 Switching sequence 33

Table 3.2 Specification of BLDC motor 37

Table 3.3 Performance of BLDC motor using three phase IBC and three phase

IBC with coupled inductor

45

Table 3.4 Speed response of BLDC motor using three phase IBC and three

phase IBC with coupled inductor

45

Table 4.1 Parameters of PSO algorithm 58

Table 4.2 Speed response of BLDC motor using PI, PD and PID controller 61

Table 4.3 Speed response of BLDC motor using PSO algorithm based PID

controller

63

Table 4.4 Comparison among the speed response of BLDC motor without

controller, with PID controller and with PSO algorithm based PID

controller

64

Table 4.5 Optimized value of PID controller parameters using PSO algorithm 64

1

CHAPTER 1

Introduction

1.1 Introduction

Now-a-days, the demand of electricity is widely increasing day by day around the world.

The depletion of fossil fuel, its undesirable impact on environment and unwillingness to use

non-renewable energy sources has increased interest to use renewable energy sources. Solar

photovoltaic (SPV) system has been focused as a very important source of energy amongst

various renewable energy sources [1].

Solar energy is a unique solution for energy crisis amongst various renewable energy

sources because of endless aspect of it such as requiring no fuel costs, pollution free,

requiring little maintenance, do not require any moving parts, noiseless operation etc. But

PV modules still have relatively low conversion efficiency [1, 2]. Many maximum power

point tracking (MPPT) techniques are used to develop the low efficiency of the solar

system. These techniques vary in many aspects [3]. Among of these, incremental

conductance algorithm is used to track more properly in the changing irradiance conditions

than perturb and observe algorithm [4].

To utilize the maximum solar power properly and transfer this maximum power from the

solar PV module to the load a compatible power DC-DC converter is necessary. Moreover,

the interest on a compatible power DC-DC converter is increasing recently for obtaining

desired power conversion in different fields like renewable energy, high and medium power

applications etc [5]. High power conversion is greatly essential to meet the system demand

in many applications. Boost converter is one kind of step up DC-DC converter which

provides higher output voltage from low input voltage. But there are some disadvantages

such as it gives large voltage and current ripples, large voltage stress on semiconductor

devices, reduce the stability and efficiency of the system etc [6, 7]. To solve these problems

many kind of techniques are studied in various fields of research arena. The current and

voltage ripple of boost converter can be minimized by increasing the value of inductor and

capacitor or inserting additional LC filter. But these will increase the size and cost of the

converter [8, 9]. Interleaving or multi-phasing methods are notable solution to solve the

problems. It is more suitable instead of connecting multiple power devices in series or

parallel [6, 10].

2

In interleaving method, converters are connected in parallel. Interleaved boost converter

(IBC) has been researched recently in various fields for its potential acceptability. The

multi-phasing interleaved boost converter gives lower voltage and current ripple, low

switching loss, faster transient response, increase efficiency etc [11, 12]. The overall system

function is become increased by increasing the number of interleaved stages [5]. The

current and voltage ripples of the converter can be further reduced by using coupled

inductor. This also helps to reduce the volume and cost of the converter circuit [13].

The uses of Brushless DC (BLDC) motor have experienced strong growth in many

applications of solar PV system like water pump, electric vehicle because of its high

efficiency, superior torque-speed characteristics, low maintenance, less noise etc [14, 15].

However, to obtain better performance from motor drive system ripple free input supply is

very essential. The stator phase currents become pulsating if input supply with high ripple is

fed to the motor and generate torque ripple [16, 17]. This torque ripple causes speed

oscillation, vibration and noise of the motor drive. Thus, degrades the performances of the

motor [18]. Speed control of BLDC motor is also important for getting desired speed

performance [19].

In this work, the performance of BLDC motor fed from solar energy using three phase IBC

with coupled inductor is analyzed. The performances of boost converter, two phase IBC,

three phase IBC and three phase IBC using coupled inductor fed from MPPT based solar

power are also analyzed and compared based on voltage and current ripples. Moreover, an

optimal tuned PID controller is developed using particle swarm optimization (PSO) to

improve the speed response of BLDC motor for the system. Simulation and analysis of the

system are carried out through MATLAB/Simulink.

1.2 Literature Review

Solar photovoltaic (PV) system is becoming a significant renewable energy source because

of its sustainability, abundance and environmental friendly nature. This energy source is

widely used in domestic, commercial and industrial applications for both standalone and

grid connected operations [20]. However, the efficiency of solar energy conversion is still

very low and the output power of solar panel is mainly dependent on solar irradiation and

temperature [2, 21]. To get maximum power from solar panel and use it properly an

3

efficient maximum power point tracking (MPPT) technique and a suitable DC-DC

converter are very important [22].

Various MPPT techniques have been developed in the research areas to extract the

maximum power as well as improve the efficiency of the solar panel such as, perturb and

observe (P&O), incremental conductance (InC), fractional open circuit voltage (Voc),

fractional short circuit current, fuzzy logic control, neural network control etc [23, 24].

These techniques vary based on several aspects such as ease of hardware implementation,

complexity, number of required sensors, cost, speed of convergence, range of effectiveness

etc [24, 25]. Among of these MPPT techniques, perturb and observe (P&O) and

incremental conductance (InC) are most popular techniques [26]. The P&O algorithm is

simple and easy to implement. But it gives slow response and fails to track MPP under rapid

changing atmospheric condition. On the other hand, incremental conductance algorithm can

track rapidly increasing and decreasing irradiance conditions with higher accuracy than

perturb and observe. Comparative analysis between incremental conductance (InC)

algorithm and P&O algorithm for photovoltaic application were presented in [4, 27, 28].

A compatible DC-DC converter plays an important role to transfer maximum power

efficiently. But they provide some major shortcomings that have a bad effect on the system

performance. Interleaving technique is used to solve the major shortcomings of the DC-DC

converters for its huge advantages like reduce voltage and current ripples, size of filter

components, increase efficiency etc [29, 30]. Now-a-days, interleaving technique is widely

employed in various converter topologies such as Buck, Buck Boost, Zeta, SEPIC and Cuk

[31-35] converter. Interleaved Boost Converter (IBC) is widely studied in various

researched fields where „n‟ numbers of boost converters are connected in parallel. They

usually used in high power conversion system and the system where low ripples are needed

so much [36]. Coupled inductors are used in the IBC topologies as coupled inductors offer

many benefits such as reduce current ripples, volume and cost of the circuit [37, 38].

Moreover, different types of soft switching techniques are studied in the IBC topologies to

minimize the switching losses of the converter [29, 39].

Two phase IBC is analyzed for solar PWM in [10] and for solar incremental conductance

algorithm based system in [40]. Two phase IBC with coupled inductor is also analyzed for

fuel cell application in [41]. They used high inductor and capacitor to reduce the current and

voltage ripples which made the circuit bulky. There are high current and voltage ripples. In

4

[42], two phase interleaved buck converter is analyzed for battery charger and had high

output voltage ripple. Again in fuel cell application, two phase IBC is employed with

constant voltage MPPT technique in [43] and high input current ripples are also obtained

here.

BLDC motor is extremely used in industrial and household applications due to its various

advantages [44]. Solar perturb and observe algorithm based two phase IBC is proposed to

run the BLDC motor in [45]. But they did not use any speed controller. They only

calculated the settling time and did not consider overshoot and rise time of the speed

response of BLDC motor. They found settling time nearly 2seconds, which is very high.

Two phase IBC with coupled inductor is analyzed for solar based pumping application in

[46]. Perturb and observe algorithm, four switch three phase inverter and induction motor

are used in this paper. They did not calculate voltage and current ripple of the IBC and also

did not consider the speed responses of induction motor. Furthermore, it is mentioned in the

paper that, the system has high oscillation. Therefore, further research is needed to properly

tune the controller for improving the performance of MPPT based solar power fed BLDC

motor.

Various forms of controllers like PI, PD, PID, fuzzy logic and hybrid controllers are used to

control and improve the speed responses of BLDC motor [47 - 49]. For optimum tuning the

parameters of the controllers, many heuristic optimization algorithms have been derived

such as GA, PSO, Ant Colony [50-52] etc. Among algorithms, PSO algorithm has gained

great attention to determine the optimal value of controller parameters for controlling the

speed response of the motor. PSO algorithm is proposed to optimally tune the parameter of

PID controller in [53, 54] and PI controller in [55] for BLDC motor speed response. Here,

they used constant DC input voltage but in the solar based system DC input supply is varied

depending on solar irradiance and temperature.

1.3 Objectives with specific aims

The main goal of this thesis is to design an optimized speed controller of the BLDC motor

for solar MPPT based system using simulation.

The following specific objectives will be taken into consideration in the present study:

a. To analyze the performance of MPPT (incremental conductance algorithm) based

solar power fed boost converter, two phase interleaved boost converter (IBC), three

5

phase interleaved boost converter (IBC) and three phase interleaved boost converter

(IBC) using coupled inductor and compare the results.

b. To analyze the performance such as speed, torque and efficiency of brushless DC

motor using interleaved boost converter with coupled inductor.

c. To design an optimized PI/PD/PID controller with genetic algorithm (GA) or

particle swarm optimization (PSO) for controlling the speed of BLDC motor in the

system.

1.4 Outline of the thesis

This thesis is organized in five chapters. The outline of these chapters is as follows:

Chapter 1 provides a general introduction of the thesis work, literature review and the

objectives with specific aims of this research.

Chapter 2 presents a brief description of interleaved boost converter with couple inductor

and solar MPPT (incremental conductance) algorithm. This chapter also presents the

performance analysis of solar MPPT based boost converter, two phase IBC, three phase

IBC and three phase IBC with coupled inductor through MATLAB/Simulink and compares

the simulation results.

Chapter 3 shows the performance such as speed, torque, efficiency, stator current etc of

BLDC motor using solar MPPT based three phase IBC and three phase IBC with coupled

inductor and also compares the results using MATLAB/Simulink.

Chapter 4 presents PSO algorithm based PID controller to improve the speed response of

BLDC motor. The speed responses such as overshoot, settling time, rise time etc of BLDC

motor using PI, PD, PID and PSO algorithm based PID controller are analyzed in this

chapter. The performance has been analyzed and compared with simulated results using

MATLAB/Simulink.

Chapter 5 contains conclusion of the thesis and few suggestions for possible future work.

6

CHAPTER 2

Performance Analysis of Solar MPPT Based Three Phase

Interleaved Boost Converter with Coupled Inductor

2.1 Introduction

Solar energy is a major renewable energy source with the potential to meet the huge

demand of energy. Many MPPT algorithms have been researched to extract the maximum

power from solar panel [26]. Interleaved boost converter is a suitable interface for various

renewable energy sources [54]. In this chapter, the operational principle of three phase

interleaved boost converter with couple inductor and solar MPPT algorithms are briefly

discussed. The performance of solar MPPT based conventional boost converter, two phase

IBC, three phase IBC and three phase with coupled inductor IBC is analyzed in this chapter

and compared the results. MATLAB/Simulink is used to simulate and analysis the

performance evaluation of the system.

2.2 Three Phase Interleaved Boost Converter with Coupled Inductor

The DC-DC converters have been received great interest in different fields. It is an

electronic circuit which converts a source of DC voltage from one level to another DC

voltage level, providing a regulated voltage output. They are basically used to step down or

step up or both step down and up an unregulated dc input voltage. In many applications like

renewable energy systems, high power conversion along with low ripple is very essential

[37, 56]. Boost converter is widely utilized to provide higher output voltage from low input

voltage. However, DC-DC converters provide sever disadvantages in the system such as

high voltage and current ripples, high voltage stress on semiconductor devices, serious

reverse recovery of diodes, reduce the stability and efficiency of the system etc [20, 9]. In

order to overcome aforementioned difficulties, several techniques are studied in various

research areas. Among them, interleaving or multi-phasing method is more suitable to

overcome the difficulties [38, 50].

The interleaved boost converter (IBC) is extremely used for its multipurpose conveniences

in different applications such as solar system [59], fuel cell system [60], electric vehicle

[61], power factor correction [62], battery charging [63] and so on. Moreover, by increasing

7

the number of interleaved stages and using coupled inductor in interleaved converter, the

performance of the system can be enhanced [14, 51]. IBC is commonly employed to

Reduce ripples of voltage and current,

Reduce the size of filter components,

Reduce switching losses,

Reduce current stresses of semiconductor devices,

Give better thermal distribution,

Give faster transient response,

Increase reliability of the converter,

Increase the efficiency etc [10, 12, 29].

The operation principle and design methodology of interleaved boost converter are

described below:

2.2.1 Operational principle of Interleaved Boost Converter (IBC)

In interleaved boost converter, „n‟ numbers of boost converters are connected in parallel

where the switching signals of the converter are operated by 2π/n radians or 360°/n phase

shifting among the switches and the same duty cycle, D [54, 67]. The „n‟ phases interleaved

boost converter circuit diagram is appended in Fig. 2.1.

Fig. 2.1 (a) Conventional boost converter and (b) „n‟ phases interleaved boost converter

8

In interleaved boost converter, the total power is divided into the numbers of paralleled

converter and each module input and output current with their ripples are reduced by 1/n [6,

54]. Phase shifting among the signals of switches is used to interleave the inductor currents

(IL) and the input current (Iin) of the converter is the sum of inductor currents. As a result, it

minimizes the ripples of input current, output current and voltage [46 - 48].

IBC is operated in two switching stages in each boost switch unit which are switch close

stages and switch open stages. When the switch is closed, the diode is blocked and the

current in the inductor start to rise and charge the inductor. When the switch is opened, the

inductor starts to discharge and transfer the current through diode to the load [10]. Inductor

currents and PWM signals of interleaved boost converter are given in fig. 2.2. In the fig.

2.2, it is for three phase IBC, there are three overlapping inductor currents IL1, IL2 and IL3

with 120° phase shift and the lower is the resultant input current Iin.

Fig. 2.2 Inductor Currents and PWM Signals of IBC (Three phase IBC)

2.2.2 Operational principle of IBC with coupled inductor

The interleaved boost converter having coupled inductors ensures

Reduce current ripples,

Reduce core size,

Reduce volume of the circuit,

9

Reduce cost of the circuit,

Increase system efficiency etc [41, 67].

The interleaved boost converter having uncoupled inductors needs two or more inductors

and cores. On the other side, the coupled inductor interleaved boost converter uses only one

core with two or more inductors [37, 41]. Three phase coupled inductor IBC circuit diagram

is shown in fig. 2.3 where three inductors L1, L2 and L3 are coupled with coupling

coefficient of k. Figure 2.4 shows the equivalent circuit of directly coupled inductor.

Fig. 2.3 Circuit diagram of three phase IBC with directly coupled inductor

Fig. 2.4 Equivalent model of directly coupled inductors

The relationships of two directly coupled inductors from fig. 2.4(b) are [41, 58]:

1 2mL k L L (2.1)

mL kL (2.2)

10

1kL k L (2.3)

Where, k is coefficient of coupling, L1 and L2 (L1= L2= L) are inductance, Lk (Lk1= Lk2= Lk)

is leakage inductance and Lm is the mutual inductance.

2.2.3 Design methodology of Interleaved Boost Converter

The „n‟ phases interleaved boost converter, shown in fig. 2.1, is followed to make two

phase, three phase and three phase using coupled inductor interleaved boost converter. For

designing the interleaved boost converter, under mentioned parameters have been utilized

[10, 40, 69]:

A) Duty Ratio (D):

If the output and input voltages of the interleaved boost converters are Vo and Vin, then the

duty ratio, D is calculated as follows which is equal to conventional boost converter.

1

ino

VV

D

(2.4)

Duty ratio can be varied from 0 to 1 which is stated as percentage or a ratio.

B) Input Current (Iin):

Input power and input voltage are used to calculate input current.

inin

in

PI

V (2.5)

Where, Pin is the input power (W) and Vin is the input voltage (V).

C) Inductor Current Ripple (ΔIL):

The amplitude of inductor current ripple for IBC and conventional boost converter are

same.

(max) (min)in

L L L

V DI

LI

fI

(2.6)

Where, inductance is L (H), switching frequency is f (Hz), input voltage is Vin (V) and duty

cycle is D.

D) Selection of Capacitor and Inductor:

The values for capacitor and inductor are calculated by using the following equations:

11

D o

o

CV R f

V

(2.7)

in

L

V DL

f I

(2.8)

Where, Vo is output voltage (V), f is frequency (Hz), R is resistance (Ω), D is duty ratio,

ΔVo is the output voltage ripple (V), Vin is the input voltage (V) and ΔIL is inductor current

ripple (A). In this thesis work, the output capacitor of the interleaved boost converters are

taken large and the inductors have same inductances, L1=L2=L3.

E) Choosing of Power Devices:

In this paper, the ideal IGBTs are taken as devices for switching. IGBTs have the combine

advantages of BJTs and MOSFETs. They are driven with the phase shift angle of 180° for

two phase and 120° for three phase interleaved boost converter.

2.3 Solar Cell and Maximum Power Point Tracking (MPPT)

Solar photovoltaic (SPV) systems are considered as one of the most reliable and matured

technologies amongst various renewable energy sources. This systems are gaining

popularity in several applications like water pumping, street lighting, solar battery charging

stations, low and high power electrical generation, grid interfaced systems, low power

electronic gadgets, hybrid SPV with other energy resources, solar vehicles and so on [4, 21,

70]. Solar PV systems are broadly classified as standalone system, grid connected system

and hybrid system [23]. However, a great deal of research has been done on maximum

power point tracking (MPPT) techniques to improve the low conversion efficiency of the

solar system. These techniques differ in complexity, required number of sensors,

convergence speed, cost, range of effectiveness, ease of hardware implementation etc [25,

71]. Some of these techniques are [23, 24, 27]:

Perturb and observe (P&O),

Incremental conductance (InC),

Fractional open circuit voltage (Voc),

Fractional short circuit current (Isc),

Ripple correlation control (RCC),

Current sweep,

Fuzzy logic control (FLC),

12

Artificial neural network (ANN),

Load current or load voltage maximization etc.

Among of these Perturb and observe (P&O) and Incremental conductance (InC) are the

most useable algorithms [26, 72].

A brief description on solar cell and incremental conductance algorithm are given below:

2.3.1 Solar cell

A solar cell is a simple p-n semiconductor junction which converts the photon energy of

sunlight into electrical energy by means of the photoelectric phenomenon. This

phenomenon is found in certain types of semiconductor materials such as silicon and

selenium. The simplified equivalent circuit of solar cell consists of a current source (Iph),

diode (D), series resistance (Rs) and shunt resistance (Rsh). The equivalent circuit of solar

cell is given below in fig. 2.5.

Fig. 2.5 Equivalent Circuit of Solar Cell

The current equation of above mention circuit is represented as [10, 63, 64]:

ph d shI I I I (2.9)

Where, Iph is photocurrent (A), Id is diode current (A) and Ish is shunt current (A).

The photocurrent, Iph is proportional to the light intensity and can be represented as,

[I K (T T )]Gph sc i op refI (2.10)

Where, Isc is cell short circuit current, Ki is short circuit current temperature coefficient, Top

and Tref are the operating temperature of the cell (K) and the reference temperature (K)

respectively and G is solar irradiance (W/m²).

The current through the diode (Id) is,

(V IR )exp 1s

d s

qI I

nKT

(2.11)

13

Where, q is charge of electron (1.6×10-19

C), V is voltage across the output terminals (V), I

is output current (A), T is temperature (K), n is diode ideality factor, K is Boltzmann

constant (1.38 × 10−23

) and Rs is series resistance (Ω). Is is reverse saturation current,

1 1( )

3T

I ( ) .T

g

ref op

qE

nK T Top

s rs

ref

I e

(2.12)

Where, Eg is the band gap energy of the semiconductor (eV) and Irs is the reverse saturation

current (A) of the diode at Top.

The shunt current can be expressed as,

ssh

sh

V IRI

R

(2.13)

Where, Rs and Rsh are series and shunt resistance (Ω) of solar cell.

Equation (2.9) can be rewritten as,

[I K (T T )]G

(V IR )exp 1

sc i op ref

s ss

sh

I

q V IRI

nKT R

(2.14)

A single solar cell can produce only a small amount of power. Solar cells and panels are

connected in parallel or series to obtain the required output power of a system. The purpose

in the series configuration is to increase the output voltage, while the parallel connection is

made to increase the current [22]. The power generated by solar panels depends on solar

irradiance, temperature, sunlight spectrum, dirt, shadow etc [3, 75].

2.3.2 Incremental Conductance Algorithm

Incremental conductance algorithm (InC) is one of the most popular and reliable MPPT

technique. It can track rapidly increasing and decreasing irradiance conditions with higher

accuracy. Incremental Conductance algorithm works depend on the change of solar power

(ΔP) to its voltage (ΔV). MPP is located when differentiation result is zero. This is shown in

following equations [23, 40],

( )0

P VI II V

V V V

(2.15)

Therefore,

I Iat theMPP

V V

(2.16)

14

I Iat theleft of MPP

V V

(2.17)

I Iat theright of MPP

V V

(2.18)

Where, I

V

is instantaneous conductance and

I

V

is incremental conductance.

According to this method, MPP are detected by comparing each step of the derivative of

conductance (ΔI/ΔV) with the instant conductance (-I/V) and by adjusts the duty cycle (D)

of the converter by increasing or decreasing the perturbation, ΔD [24, 40]. This algorithm

determines the direction of perturbation based on the slope of power-voltage (PV) curve.

The power-voltage (PV) curve is shown in Fig. 2.6 where, the slope is zero at MPP, positive

on the left and negative on the right of MPP.

Fig. 2.6 Power-Voltage Characteristics of PV Module

The Incremental conductance algorithm flowchart is shown in fig. 2.7 [27, 40]. According

to this flow chart, ΔV and ΔI can be calculated by sensing the solar voltage and current.

Then, by comparing the incremental conductance and the instantaneous conductance, it

adjusts the duty cycle (D) of the converter.

2.4 Simulation and Analysis

The simulation of the system is carried out through MATLAB/Simulink. The simulated

results are used to evaluate the performance of the system. This section shows the

simulation model of the system, parameters of necessary system components and the results

obtained from simulation.

15

Fig. 2.7 Flowchart of Incremental Conductance (InC) algorithm

2.4.1 MATLAB/Simulink model of solar MPPT based three phase IBC with coupled

inductor

The diagram of solar MPPT based three phase IBC with coupled inductor using

MATLAB/Simulink is given in fig. 2.8. In fig. 2.8, coupled inductor three phase interleaved

boost converter is fed from solar energy. This converter is controlled by incremental

conductance algorithm.

16

Fig. 2.8 MATLAB simulation model of solar MPPT based three phase IBC with coupled

inductor

The incremental conductance algorithm with 120° phase shifting PWM signal generation

model is shown in fig. 2.9.

2.4.2 System component

The major components of the system, shown in fig. 2.8, are solar PV module and three

phase IBC with coupled inductor. It is important to know the parameters of all the necessary

components to analyze the system.

17

Fig. 2.9 MATLAB simulation model of incremental conductance algorithm with PWM

signal generation.

A) Solar PV module

The specification of solar panel (Solarex MSX60) [26, 40] is listed in table 2.1. The solar

panels are standardized under Standard Test Condition (STC) at air mass 1.5, irradiance

1000W/m2 and cell temperature 25°C.

Table 2.1 Specification of solar panel (Solarex MSX60)

Parameters Symbols Values Units

Typical peak power Pmpp 60 W

Voltage at peak power Vmp 17.1 V

Current at peak power Imp 3.5 A

Open circuit voltage Voc 21.1 V

Short circuit current Isc 3.8 A

Temperature co- efficient of Isc (0.065±0.015)%/°C

Temperature co-efficient of Voc -(80±10)mV/ C

Temperature coefficient of power -(0.5±0.05)%/°C

18

Parameters Symbols Values Units

Nominal operating cell

temperature (NOCT2)

47±2°C

B) Three phase IBC with coupled inductor

The parameters of three phase interleaved boost converter using uncoupled and coupled

inductor are listed in table 2.2. Three ideal IGBTs are taken as devices of switch. The duty

cycle is 57.7%.

Table 2.2 Parameters of three phase IBC (uncoupled & coupled inductor)

Parameters Symbols Values Units

Input voltage Vin 21.07 V

Switching frequency F 25 kHz

Self inductors L1,L2,L3 0.48 mH

Coupling coefficient K 0.66

Mutual inductance Lm 0.36 mH

Capacitor C 20 μF

Resistor R 4.8 Ω

2.4.3 Simulation outputs and analysis

The model of solar MPPT based three phase IBC with coupled inductor, shown in fig 2.8, is

followed to make comparative analysis among boost converter, two phase and three phase

IBC and three phase uncoupled and coupled interleaved boost converter. MATLAB

simulation outputs of boost converter, two phase IBC, three phase IBC using uncoupled and

coupled and their analysis are given below:

A) IV and PV characteristics curve of solar panel

The IV and PV characteristics curve of solar panel are given in fig. 2.10

19

Fig. 2.10 (a) IV and (b) PV characteristics curve of solar panel

B) Switching pulses

Switching pulses of boost converter, two phase IBC and three phase IBC are shown in fig.

2.11, when duty cycle is 57.7%. The switching pulses of three phase IBC having uncoupled

and coupled inductor are same. The phase shift angle is 180° for two phase and 120° for

three phase IBC.

20

Fig. 2.11 Switching pulses of a) Boost converter, b) Two phase IBC with 180° phase shift

and c) Three phase IBC (uncoupled and coupled inductor) with 120° phase shift

C) Inductor currents

Inductor currents of boost converter, two phase IBC and three phase IBC (uncoupled and

coupled inductor) are appended in fig 2.12. In fig 2.12(b), fig 2.12(c) and fig 2.12(d), we

can see the overlapping inductor currents of IBC with 180° phase shift and 120° phase shift

respectively.

21

22

Fig. 2.12 Inductor current waveforms of (a) boost converter (b) two phase IBC (c) three

phase IBC and (d) three phase IBC with coupled inductor

D) Input current ripple

The ripples of input current are very poor by increasing the phases of IBC and by using

coupled inductor than conventional boost converter shown in fig 2.13. The ripple of input

current of boost converter is 1.13A, two phase IBC is 0.302A, three phase IBC is 0.28A and

coupled inductor three phase IBC is 0.13.

23

Fig. 2.13 Input current ripple waveforms of (a) boost converter (b) two phase IBC (c) three

phase IBC and (d) three phase IBC with coupled inductor

24

E) Output current ripple

The output current ripples of different interleaved boost converters are shown in fig 2.14

where output current ripple of boost converter is 2.66A, two phase IBC is 0.33A, three

phase IBC is 0.22A and coupled inductor three phase IBC is 0.18A.

25

Fig. 2.14 Output current ripple waveforms of (a) boost converter (b) two phase IBC (c)

three phase IBC and (d) three phase IBC with coupled inductor

F) Output voltage ripple

The output voltage ripple for boost converter is 12.168V, two phase IBC is 1.602V, three

phase IBC is 0.9V and coupled inductor three phase IBC is 0.87V. We can see that the

ripples of output voltage are become decrease. They are shown in fig 2.15.

26

Fig. 2.15 Output voltage ripple waveforms of (a) boost converter (b) two phase IBC (c)

three phase IBC and (d) three phase IBC with coupled inductor

27

To make comparison, the performance of conventional boost converter and IBC using two

and three phase are listed in the table 2.3 and the performance of three phase IBC and three

phase IBC with coupled inductor is tabulated as table 2.4 given below.

Table 2.3 Performance of conventional boost converter, two and three phase IBC

Parameters Boost

converter

Two phase

IBC

Three phase

IBC

Input voltage (V) 21.07 21.07 21.07

Output voltage (V) 54.36 49.57 48.09

Input current ripple (A) 1.13 0.302 0.28

Output current ripple (A) 2.66 0.33 0.22

Output voltage ripple (V) 12.168 1.602 0.9

Table 2.4 Comparison between uncoupled and coupled inductor three phase IBC

Parameters Three phase

uncoupled IBC

Three phase

directly

coupled IBC

Input voltage (V) 21.07 21.07

Output voltage (V) 48.09 48.06

Input current ripple (A) 0.28 0.13

Output voltage ripple (V) 0.9 0.87

Output current ripple (A) 0.22 0.18

2.5 Conclusion

Interleaved boost converter have recently attracted much attention due to their various

advantages. The performance of boost converter, two phase IBC, three phase IBC and three

phase IBC using coupled inductor is analyzed and compared the results in this chapter by

using MATLAB/Simulink. The converters are fed from MPPT (incremental conductance

algorithm) based solar energy. From MATLAB/Simulink simulation it is found that, the

input current ripple of boost converter is 1.13A, two phase IBC is 0.302A, three phase IBC

is 0.28A and three phase IBC with coupled inductor is 0.13A. The output current ripple of

28

boost converter is 2.66A, two phase IBC is 0.33A, three phase IBC is 0.22A and three phase

IBC with coupled inductor is 0.18A. The output voltage ripple for two phase IBC is 1.602V,

three phase IBC is 0.9V and three phase IBC with coupled inductor is 0.87V while the

output voltage ripple of boost converter is 12.168V. From the simulation results it can be

observed that, the current and voltage ripples of IBC is notably improved by increasing the

number of phases and by using coupled inductor. In this analysis it can be mentioned here

that, the three phase IBC using coupled inductor shows better performance compared to

other converters.

29

CHAPTER 3

Performance Analysis of BLDC Motor Using Three Phase

Interleaved Boost Converter with Coupled Inductor

3.1 Introduction

Brushless DC motors are extensively used in many applications such as water pumps,

electric and hybrid electric vehicles, robotics, automotive electronics, space satellite,

household appliances etc [16, 78]. In previous chapter, it is observed that, the system using

three phase IBC with coupled inductor provides better performance in term of current and

voltage ripples. Thus the performance of brushless DC motor fed from MPPT based solar

energy using coupled inductor three phase interleaved boost converter is presented in this

chapter. The performance such as speed, torque, efficiency, stator current etc of BLDC

motor using three phase IBC with coupled inductor are analyzed using MATLAB/Simulink.

The comparison between three phase IBC fed BLDC motor and coupled inductor three

phase IBC fed BLDC motor is carried out in this chapter through simulation results.

3.2 Brushless DC (BLDC) motor

Brushless DC motor is one type of permanent magnet synchronous motor. Its physical

appearance is similar to a three phase permanent magnet synchronous motor and its

operating characteristic is similar to those of a DC motor [77]. The uses of BLDC motor

have increased in different applications due to its various advantages likely:

High efficiency,

High power density,

High power factor,

Increased reliability,

Superior torque-speed characteristics,

Low maintenance,

Noiseless operation,

Higher speed ranges,

Easy to control,

Long operating life etc [14, 15, 78].

30

Its armature is in the stator and permanent magnets are on the rotor. It gives trapezoidal

back EMF. BLDC motors come in single-phase, 2-phase and 3-phase configurations.

Among them, three phase BLDC motor is used widely.

BLDC motor utilizes electronic commutation instead of using a mechanical commutator

and brushes as in the conventional DC Motor. The Brushes of the mechanical commutator

eventually wear out and need to be replaced. Due to the absence of brushes and

commutator, there is no problem of mechanical wear in the moving parts of the BLDC

motor. In this motor, an inverter is used to control the action of commutator and proficient

at wide range of speed control using pulse width modulation (PWM). Position sensor like

hall sensor is used to determine the rotor position. Figure 3.1 shows a transverse section of a

BLDC motor [79]. Three phase power supply is given to stator from an inverter. The BLDC

motor is driven by the inverter which is fed by a DC source and converts dc voltage to three

phase AC voltage whose frequency corresponds to the speed of the rotor. The circuit of

BLDC motor with three phase inverter is appended in fig 3.2.

Fig. 3.1 Transverse section of BLDC motor

3.2.1 Operation principle of brushless DC motor

The operating characteristic of BLDC motor is similar to DC motor. When current flows

through one of the three stator windings by a supply source, it generates electromagnetic

field and becomes an electromagnet. The generated magnetic pole attracts the nearest

permanent magnet of the opposite pole. So the rotor moves towards the energized stator. In

order to rotate the BLDC motor, the stator windings should be energized in a sequence.

Which winding is energized depend on rotor position. Rotor position is sensed using Hall

31

Effect sensors. Three Hall Effect sensors are embedded into the stator at 120 degrees

interval on the non-driving end of the motor. Whenever the rotor magnetic poles pass near

the Hall sensors, they give a high or low signal, indicating the N (North) or S (South) pole is

passing near the sensors. The signals from the Hall sensors produce a three digit number

(H1, H2, H3) that changes every 60° electrical as shown in figure 3.3 below [19, 75].

Fig. 3.2 Circuit diagram of BLDC motor with three phase inverter

Fig. 3.3 Back EMFs, phase currents and Hall sensor signals for each phase

32

Each switching state has one of the windings energized to positive power (current enters

into the winding), the second winding is negative (current exits the winding) and the third is

in a non-energized condition. Torque is produced because of the interaction between the

magnetic field generated by the stator coils and the permanent magnets. The peak torque

occurs when these two fields are at 90° to each other and falls off when the fields move

together. The Hall sensor signal has the rising edge and the falling edge for each phase. That

generates six trigger signals per cycle. The conduction action is done by three phase inverter

where switches are IGBT or MOSFET. Each switch is conducted for 120° per cycle so that

each phase carries current only during the 120° period. The switching sequence, current

direction and position sensor signals are shown in table 3.1 [80].

Table 3.1 Switching sequence

Switching

interval

in degree

Sequence

number

Position sensors Active

switches

Phase current

H1 H2 H3 A B C

0-60 1 1 0 1 S1 S6 + - OFF

60-120 2 1 0 0 S1 S2 + OFF -

120-180 3 1 1 0 S3 S2 OFF + -

180-240 4 0 1 0 S3 S4 - + OFF

240-300 5 0 1 1 S5 S4 - OFF +

300-360 6 0 0 1 S5 S6 OFF - +

3.2.2 Mathematical model of BLDC motor

The BLDC motor has three star connected stator winding and a permanent magnet rotor. It

has no damper winding. The circuit equations of the three windings in phase variables are as

follows [79, 81-83]:

0 0

0 0

0 0

as s as aa ab ac as as

bs s bs ba bb bc bs bs

cs s cs ca cb cc cs cs

V R i L L L i ed

V R i L L L i edt

V R i L L L i e

(3.1)

33

Where,

Vas, Vbs and Vcs are the phase voltages of stator winding (V),

ias, ibs and ics are the stator phase currents (A),

Rs is the stator resistance per phase (Ω),

Laa, Lbb and Lcc are the self-inductance of phase a, b and c respectively (H),

Lab, Lbc and Lac are mutual inductance between phases a, b and c (H),

eas, ebs and ecs are phase back EMF (V) which are trapezoidal shaped.

It has been assumed that, the rotor reluctance with angle does not change due to non-salient

rotor. Then,

aa bb ccL L L L (3.2)

= ab ba ac ca bc cbL L L L L L M (3.3)

By Substituting Eq. (3.2) and Eq. (3.3) in Eq. (3.4),

0 0

0 0

0 0

as s as as as

bs s bs bs bs

cs s cs cs cs

V R i L M M i ed

V R i M L M i edt

V R i M M L i e

(3.4)

The phase currents of stator are considered to be balanced,

0as bs csi i i (3.5)

. . .bs cs asM i M i M i (3.6)

By using eq. (3.6) in eq. (3.4),

0 0 0 0

0 0 0 0

0 0 0 0

as s as as as

bs s bs bs bs

cs s cs cs cs

V R i L M i ed

V R i L M i edt

V R i L M i e

(3.7)

The phase back EMF can be written as,

r

r

r

( )

( )

( )

as as

bs m m bs

cs cs

e f

e f

e f

(3.8)

Where, angular rotor speed is ωm (radians per second), flux linkage is λm, rotor position is θr

(radian) and fas (θr), fbs (θr) and fcs (θr) are the flux function of rotor position having the

same shape as back EMF with a maximum value of ± 1.

34

The electromagnetic torque is given by,

as as bs bs cs cse

m

e i e i e iT

(3.9)

If the inertia is J (kg.m2), coefficient of friction is B (N.m.s/rad) and load torque is TL (N.m),

then the equation of motion for the system is,

e L(T T )mm

dJ B

dt

(3.10)

The relation of electrical rotor position and mechanical speed is,

2

rm

d P

dt

(3.11)

Where, P is number of poles and the rotor position is θr.

3.3 Block diagram of solar MPPT based three phase IBC with coupled

inductor for BLDC motor

The block diagram of the system to analyze the performance of the BLDC motor using

three phase IBC with coupled inductor is given below in fig. 3.4. The converter is fed from

MPPT (InC. algorithm) based solar energy.

Fig. 3.4 Block diagram of solar MPPT (incremental conductance algorithm) based three

phase IBC with coupled inductor for BLDC motor

35

In fig 3.4, we can see that, BLDC motor is fed from solar energy using three phase IBC

with coupled inductor. This converter is controlled by incremental conductance algorithm.

Hall sensors are used to determine the rotor position. And an inverter is used to control the

action of commutator.

3.4 Simulation and Analysis

The performance of BLDC motor such as speed, torque, efficiency etc using three phase

IBC and three phase IBC with coupled inductor is analyzed by using MATLAB/Simulink.

The simulation of the system and its simulation results are described below.

3.4.1 MATLAB/Simulink model of solar MPPT based three phase IBC with coupled

inductor for BLDC motor

MATLAB/simulink is used to simulate the system, shown in fig 3.4. The simulation model

is shown in fig 3.5.

Fig. 3.5 MATLAB simulation model of solar MPPT (incremental conductance algorithm)

based three phase IBC with coupled inductor for BLDC motor

36

It is observed in fig 3.5 that, the solar power is transferred to the BLDC motor using three

phase IBC. Three inductors are coupled directly in three phase IBC. The rotor position is

sensed by Hall Effect sensor. According to the rotor position, the commutation action is

done by three phase inverter where six MOSFET switches are used.

3.4.2 System component

The major components of the system, shown in fig. 3.5, are solar PV module, three phase

IBC with coupled inductor and BLDC motor. Here, the solar PV module and three phase

IBC with coupled inductor are used which are already followed in previous chapter at

section 2.3.2. The specification of the BLDC motor is in table 3.2.

Table 3.2 Specification of BLDC motor

Parameters Values Units

No. of poles 8

Rated voltage 48 V

Rated current 10 A

Rated speed 145 rad/s

Rated torque 1.5 Nm

Resistance/Phase 0.46 Ω

Self inductance 2.1 mH

Mutual inductance 1.2 mH

Flux linkages constant 0.105 V-s/rad

Torque constant 0.40 V-s/rad

Moment of inertia 0.0048 Kg-m2

Damping constant 0.002 N-m/rad/sec

3.4.3 Simulation outputs and analysis

Figure 3.5, the MATLAB/Simulink model of solar MPPT based three phase IBC with

coupled inductor for BLDC motor, is followed to analyze the performance such as speed,

torque, efficiency etc of BLDC motor using three phase IBC and three phase IBC with

coupled inductor. The performance of brushless DC motor is analyzed with no load and

37

without using any speed controller. MATLAB simulation outputs and their analysis are

given below:

A) Hall Effect signals and gate pulses

Three Hall Effect sensors are used to sense the rotor position by giving digital signals. The

output of Hall sensors changes for every 60° electrical. After decoding them, it delivers six

gate signals which are used to switch the inverter MOSFETs. The conduction period of each

switch is 120º. Figure 3.6 shows the Hall sensor outputs and the gate pulse waveforms are

shown below in fig 3.7.

Fig. 3.6 Hall sensor outputs

38

Fig. 3.7 Gate pulse waveforms

B) Speed

The speed of three phase IBC fed BLDC is 258.1rpm as shown in fig 3.8. It shows rise time

of 0.0043s, peak time of 0.0099s, peak overshoot of 50.64% and remains steady after

0.1126s. The speed waveform of BLDC motor using three phase IBC with coupled inductor

is given in fig 3.9. From the fig, we can see that, overshoot also presents in the speed

waveform that is 50.46% and after 0.1043s it reaches to the steady state position. The rise

time is 0.0038s, peak time is 0.0088s and the speed of coupled inductor three phase IBC fed

BLDC is 258.4rpm.

39

Fig. 3.8 Speed waveforms of BLDC motor using three phase IBC

Fig. 3.9 Speed waveforms of BLDC motor using three phase IBC with coupled inductor

C) Stator currents

At the time of starting, the stator phase current of BLDC motor using three phase IBC is

very high, 20.54A and after 0.1033s the level of current decreases and stabilizes as shown in

fig 3.10. Whereas, the starting current of coupled inductor three phase IBC fed BLDC

motor is 18.8A and stabilizes at 0.1033s, appended in fig 3.11.

40

Fig. 3.10 Stator phase current waveforms of BLDC motor using three phase IBC

Fig. 3.11 Stator phase current waveforms of BLDC motor using three phase IBC with

coupled inductor

41

D) Back EMFs

The back EMFs of BLDC motor using three phase IBC and coupled inductor three phase

IBC are given in fig 3.12 and fig 3.13 respectively. It is observed from figures that, the back

EMFs of BLDC motor are trapezoidal in shape.

Fig. 3.12 Back EMF waveforms of BLDC motor using three phase IBC

Fig. 3.13 Back EMF waveforms of BLDC motor using three phase IBC with coupled

inductor

42

E) Torque

The torque is high at the time of starting BLDC motor without load. After that, the torque

reduces and reaches at a stable position. The torque waveforms of BLDC motor and their

ripples are shown below in fig 3.14 and fig 3.15. It is found from the figures that, the torque

of three phase IBC fed BLDC motor has ripples of 1.58Nm. But in coupled inductor three

phase IBC fed BLDC motor, torque contains less ripples, 1.41Nm.

Fig. 3.14 (a) Torque waveform and (b) ripples in torque waveform of BLDC motor using

three phase IBC

43

Fig. 3.15 (a) Torque waveform and (b) ripples in torque waveform of BLDC motor using

three phase IBC with coupled inductor

From above simulation output, the performance such as stator current, speed, torque etc of

BLDC motor using three phase IBC and coupled inductor three phase IBC are listed below

in table 3.3 to make comparison.

Besides, a comparison of speed response parameters such as rise time, settling time, peak

time and percentage of peak overshoot between three phase IBC fed BLDC motor and

coupled inductor three phase IBC fed BLDC motor are tabulated in table 3.4 below.

44

Table 3.3 Performance of BLDC motor using three phase IBC and three phase IBC with

coupled inductor

Parameters

Three phase

IBC fed BLDC motor

Three phase

coupled inductor IBC fed

BLDC motor

Starting current of

stator (A)

20.54

Settled at 0.1033s

18.8

Settled at 0.1033s

Torque ripple (Nm) 1.58 1.41

Speed (rpm) 258.1

Settled at 0.1126s

258.4

Settled at 0.1043s

Table 3.4 Speed response of BLDC motor using three phase IBC and three phase IBC with

coupled inductor

Parameters Three phase

IBC fed BLDC motor

Three phase

coupled inductor IBC fed

BLDC motor

Rise time (s) 0.0043 0.0038

Peak time (s) 0.0099 0.0088

Settling time (s) 0.1126 0.1043

Percentage overshoot

(% OS)

50.64 50.46

3.5 Conclusion

The performance such as stator current, speed, torque etc of BLDC motor using three phase

IBC and three phase IBC with coupled inductor is analyzed in this chapter. Their

performances have been analyzed and compared with simulated results using

MATLAB/Simulink. This analysis has done with no load condition and without using any

speed controller. It is seen from the simulation results that, the starting current of coupled

inductor three phase IBC fed BLDC motor is 18.8A and become stable at 0.1033s, torque

ripple is 1.41Nm and speed is 258.4rpm which settled at 0.1043s. Whereas the three phase

IBC fed BLDC motor gives starting current of 20.54A and stable at 0.1033s, torque ripple

1.58Nm and speed of 258.1rpm at 0.1126s. In addition, the speed performance of coupled

45

inductor three phase IBC fed BLDC motor shows the rise time of speed is 0.0038s, peak

time is 0.0088s, settling time is 0.1043s and peak overshoot is 50.46%. While three phase

IBC fed BLDC motor gives the rising time of 0.0043s, peak time of 0.0099s, settling time

of 0.1126s and overshoot of 50.64% in the speed waveforms. It is observed that, compared

to three phase IBC fed BLDC motor, the coupled inductor three phase IBC fed BLDC

motor shows better performance.

46

CHAPTER 4

Speed Control of BLDC Motor Using PSO Algorithm

4.1 Introduction

A suitable speed controller is very essential to achieve the desired level of motor

performance. PID controller is one of the most used speed controller to improve the speed

response of BLDC motor [84]. The parameters of PID controller must be well tuned in

order to obtain better performance such as, reduce overshoot, steady state error, speed up

the response etc. There are several methods have been derived in the research field for

tuning the parameters of PID controller but they require a long time as well as its difficult to

find the optimal values of PID parameters, provide inferior performance in complex and

nonlinear systems etc [85, 86]. Many evolutionary optimization algorithms have been used

for optimal tuning of PID controller parameters. Among them, particle swarm optimization

algorithm (PSO) has received much interest because of its faster convergence rate,

simplicity, good performance etc [87, 88]. Thus, in this chapter, PSO algorithm based PID

controller is used in three phase IBC with coupled inductor fed BLDC motor to improve the

speed response of BLDC motor. MATLAB/Simulink is used to analyze the speed response

parameters such as overshoot, settling time, rise time etc of BLDC motor using proposed

controller in this chapter. These simulation result are compared among that of the speed

response of BLDC motor with PID controller and without using any controller. The speed

response of BLDC motor using PI controller and PD controller are also analyzed in this

chapter.

4.2 PI controller

PI (Proportional plus Integral) controller is one of the most used controllers in recent years

with the purpose of improving the transient and the steady-state performance. It comprises

of proportional action and integral action. The steady-state error can be improved by this

controller. The integral action eliminates the error which is introduced by the proportional

control. By using PI controller, the steady state error can be brought down to zero and the

transient response can be improved simultaneously. The formula of PI controller can be

expressed as [83, 89]:

0(t) (t) K (t)dt

t

P iu K e e (4.1)

Where, KP is the proportional gain, Ki is the integral gain and e(t) is the error signal.

47

The error signal, e(t) is the difference between the desired input value r(t) and the actual

output value y(t) and can be defined as:

(t) r(t) y(t)e (4.2)

4.3 PD controller

The combination of derivative control action and proportional control action is termed as

PD (Proportional plus Derivative) controller. This controller is used to improve transient

response but it cannot reduce the steady state error to zero. The formula of PD controller

can be defined as [48, 91]:

(t) (t) K (t)P d

du K e e

dt (4.3)

Where, KP is proportional gain, Kd is derivative gain and e(t) is the error signal.

4.4 PID controller

The PID (Proportional plus Integral plus Derivative) controllers have been widely used to

solve various control problems in industrial control systems. The improvement of both

steady state error and transient response can be obtained by using PID controller. This

controller is mostly used to control the feedback loops. The PID controller has the ability to

use three control terms of proportional, integral and derivative to provide accurate and

optimal control of a system. This controller is also known as three term controller. The

formula of PID controller is described by the following equation [83]:

p0

1 (t)(t) K [e(t) ( )d ]

t

d

i

deu e T

T dt (4.4)

Where, Kp is the proportional gain, Ti is the integral time, Td is referred as the derivative

time, t is the instantaneous time, τ is the variable of integration (takes on values from time 0

to the present t) and e(t) is error signal.

The block diagram of PID controller based closed-loop control system is shown in fig. 4.1

where it has been seen that in a PID controller , the error signal e(t) is used to generate the

proportional, integral, and derivative actions. The controller gives a control signal, u(t) by

weighted sum of the resulting signals of the control terms. This control signal, u(t) is

applied to the plant model.

48

Fig. 4.1 Block diagram of PID controller based system

The three control terms i.e. proportional, integral and derivative controls of PID controller

are given below [93]:

4.4.1 Proportional control

Proportional control in the PID controller is denoted as P-term. It provides an output value

that is proportional to the current error value to control the system. The proportional part of

the PID controller introduces an offset error into the system. The proportional control is

given by,

pK e(t)termP (4.5)

Where, e(t) is the error signal and Kp is the proportional gain.

4.4.2 Integral control

Integral control in the PID controller is denoted by I-term. The output of integral term is

proportional to both the magnitude of the error and the duration of the error. Integral control

accelerates the movement of the process output towards set-point in steady state. It

eliminates the offset error that occurs with a pure proportional control without the use of

excessively large controller gain but it may make the transient response worse. The integral

control is given by,

0K ( )d

t

term iI e (4.6)

Where, Ki is the integral gain.

49

4.4.3 Derivative control

The D-term in the PID controller is the derivative control. Derivative control uses the rate of

change of an error signal. The output is calculated by multiplying this rate of change by the

derivative gain Kd. Derivative control has the effect of increasing the stability of the system,

reducing the overshoot and improving the transient response. The derivative control is given

by,

term

(t)D Kd

de

dt (4.7)

Where, Kd is the derivative gain.

The four major characteristics of the closed-loop step response are given below and shown

in fig 4.2 [90]:

A) Rise time (tr): The rise time is the time required for the response to rise from 10% to

90%, 5% to 95%, or 0% to 100% of its final value. For underdamped systems, the 0% to

100% rise time is normally used. For overdamped systems, the 10% to 90% rise time is

commonly used.

B) Peak time (tp): The peak time is the time required for the response to reach the first peak

of the overshoot.

Fig 4.2 Step response of a control system

50

C) Maximum (percent) overshoot (Mp or % OS): The maximum overshoot is the

maximum peak value of the response curve measured from unity (steady state value). It is

defined by

p(t ) c( )% 100%

c( )p

cM or OS

(4.8)

D) Settling time (ts): The settling time is the time required for the response curve to reach

and stay within a range (usually 2% or 5%) of its final value.

4.5 Particle swarm optimization (PSO) algorithm

Particle swarm optimization (PSO) is a stochastic optimization technique based on swarm

intelligence. It was first introduced in 1995 by James Kennedy and Russell Eberhart [94].

This method is motivated by the observation of social interaction and animal behaviors such

as fish schooling and bird flocking in search of food. In PSO, a swarm consists of a number

of particles. The particles are move around in the search space for the optimum solution.

Each particle adjusts its flying status according to its own flying experience and the flying

experience of its other neighboring particles. The performance of each particle is measured

according to a pre-defined fitness function, which is related to the problem being solved.

PSO is a population-based evolutionary computation technique. It is easy to implement and

has stable convergence characteristic with good computational efficiency. There is no need

to know the gradient information of the response of a system. This modern heuristic

algorithm is robust in solving continuous nonlinear optimization problems. Within shorter

time, it can generate a high quality solution [58, 85, 93]. Because of its various advantages,

the PSO is suitable for scientific research, engineering applications like, power systems,

industrial electronics, engineering system optimization and many other fields [69, 78].

In PSO algorithm, the system is initialized with a population of random solutions which are

called particles. Each potential solution of the model parameters is given a random velocity

and a random position. The particles are randomly distributed on the search space. Each

particle keeps track of its own previous best position which is called pbest. Also keeps track

of the previous best position attained by any member of its neighborhood among all the

particles. This value is called gbest. Then the particle tries to update its position by using the

information of current position, current velocity, distance between the current position and

51

previous own best position and distance between the current position and previous global

best position. The fitness function evaluates the performance of particles to determine

whether the best fitting solution is achieved and by iteratively trying to improve the

solutions. This process is iterated until the desired stopping criterion is achieved. Usually,

the stopping criterion is a relatively good fitness or a maximum number of iterations.

If D is the dimension of the search space and the current position of ith particle of the

swarm is xi = [xi1, xi2,… xiD] then the best previous position ever visited by the particle is

represented as pbesti =[pbesti1, pbesti2,…, pbestiD]. The best position obtained this far by

any particle in the population is represented by gbest. vi=[v i1,v i2,….viD] represents the

velocity of ith particle. The velocity and position of each particle is updated as follows [87,

95]

1 1 iD iD

2 2 iD

(k 1) w. (k) . ().(pbest (k) x (k))

. ().(gbest(k) x (k))

iD iDv v c rand

c rand

(4.9)

iD iD(k 1) x (k) v (k 1)iDx (4.10)

Where, D=1,2,…,m and i= 1,2,…,n. n is the number of particles in a swarm, k is the number

of iteration, w is the inertia weight factor, c1 and c2 are the cognitive parameter and social

parameter respectively and rand1 () and rand2 () are random numbers uniformly distributed

in the range from 0 to1. The concept of modification of a searching point by PSO is shown

in fig. 4.3 for two dimensional spaces. The flowchart of PSO algorithm is given in fig. 4.4.

Fig. 4.3 Concept of modification of a searching point by PSO algorithm

52

Fig. 4.4 Flowchart of PSO algorithm

53

4.6 PSO algorithm based PID controller for the system

Properly tuned PID controller is very important to obtain desired output response of a

system. In this work, the three parameters Kp, Ki and Kd of PID controller are tuned

optimally by using PSO algorithm for speed control of BLDC motor. Here, the search space

is three dimensional and the position and velocity are represented by matrices. Every

possible set of controller parameter values in the search space is represented as a particle

whose values are adjusted by minimizing the error. The error is difference between the

actual speed of motor and the reference speed and it is the objective function or fitness

function in this case. The algorithm iteration will stop when it reaches a pre-defined

maximum number of iteration.

The steps of parameter optimization of PID controller by using PSO algorithm are

explained as follows:

Step 1: Initialize the particles i.e. three parameters Kp, Ki and Kd of PID controller, with

random positions and velocities in the search space.

Step 2: Evaluate the value of fitness function i.e. error for each particle.

Step 3: Compare particle's fitness value with particle's pbest. If current value is better than

pbest, then set pbest value equal to the current value and the pbest location equal to the

current location.

Step 4: Compare pbest that has the lowest value of error among all particles in the current

iteration with the population's overall previous best, gbest. If current value is better than

gbest, then reset gbest to the current particle's value and location.

Step 5: Update the position and velocity of the particle according to equations 4.9 and 4.10,

respectively.

Step 6: If the number of iterations reaches the maximum, then go to Step 7. Otherwise, go

to Step 2.

Step 7: The particle that generates the latest gbest is an optimal controller parameter at

minimum value of error.

The flowchart of PID controller tuning by using PSO algorithm for the system is appended

in fig. 4.5.

54

Fig. 4.5 Flowchart of PID controller tuning by using PSO algorithm for the system

55

4.7 Block diagram of the system

The block diagram of the system is shown in fig. 4.6. It is observed from fig. 4.6 that, the

BLDC motor is fed from solar energy using three phase IBC with coupled inductor as

shown before in chapter 3. To control the speed response such as overshoot, settling time,

rise time etc of BLDC motor, a PID controller is used. The PID controller controls the speed

response by measuring the actual speed of the motor and compared it with a reference

speed. Particle swarm optimization (PSO) is used to optimally tune the parameters Kp, Ki

and Kd of PID controller. PSO algorithm uses the error signal and by iteratively minimizing

the value of error, it provides optimal value of PID controller parameters.

Fig. 4.6 Block diagram of the system

4.8 Simulation and Analysis

The PSO algorithm based PID controller is used in this research work for controlling and

improving the performance of the speed response of BLDC motor as required. Simulation

of the system is done through MATLAB/Simulink environment and analyzed various speed

parameters like rise time, settling time, percentage overshoot etc. The system simulation

and the simulation results are described in this section.

56

4.8.1 MATLAB/Simulink model of the system using PSO algorithm based PID

controller

The speed response of the BLDC motor is controlled by PSO algorithm based PID

controller. The BLDC motor is fed from MPPT based solar energy using three phase IBC

with coupled inductor. The diagram of the overall system using MATLAB/Simulink is

given in fig. 4.7.

Fig. 4.7 MATLAB simulation model of the system using PSO algorithm based PID

controller

In the fig. 4.7 it is seen that, the speed of BLDC motor is controlled in a closed loop. The

error signal is generated by measuring the actual speed of the motor and compared it with a

reference speed. This error signal is given as input to the PID controller. The PSO algorithm

is utilized for tuning optimal PID controller parameters. This PSO based PID controller

improve the performance of speed response of BLDC motor. The model of PID controller

using MATLAB/Simulink is shown in fig. 4.8.

57

Fig. 4.8 MATLAB simulation model of PID controller

In this system, the reference speed of the BLDC motor is set to 1000rpm. To start working

with PSO algorithm, some parameters need to be defined. The selection of these PSO

algorithm parameters is very important to get optimum results. The parameters of PSO

algorithm considered for this optimization process are shown below in table 4.1. The PSO

algorithm used for this work is coded in MATLAB environment by using these parameters

and given in Appendix.

Table 4.1 Parameters of PSO algorithm

Parameters Value

Number of variables 3

Population size 5

Maximum iteration 25

Inertia weight, w 0.11

Cognitive parameter, c1 2

Social parameter, c2 2

4.8.2 Simulation outputs and analysis

Figure 4.7 is followed to analyze the performance of speed response such as rise time,

overshoot, settling time etc of BLDC motor using PID controller and PSO algorithm based

PID controller. The speed response of BLDC motor using PI controller and PD controller

are also analyzed for this system. The simulation results of speed response of BLDC motor

are described below.

58

A) Speed response of BLDC motor using PI controller

In this system, the speed response of BLDC motor is analyzed using PI controller. Here, the

value of gain parameters Kp and Ki are tuned manually using MATLAB/Simulink. The

speed response of BLDC motor using PI controller is shown in fig. 4.10 and the speed

response of BLDC motor without using any controller is shown in fig. 4.9. It is seen from

fig. 4.10 that, the steady state error is completely minimized by using PI controller. Here,

the speed of BLDC motor is 1000rpm, overshoot is 18.3919% and became steady after

0.0485s, rise time is 0.0034s and peak time is 0.0130s. The speed response of BLDC motor

without using any speed controller in the system, shown in fig. 4.9, gives speed of

258.4rpm, rise time of 0.0038s, peak time of 0.0088s, settling time of 0.1043s and

overshoot of 50.46%.

Fig. 4.9 Speed response of BLDC motor without using any controller

59

Fig. 4.10 Speed response of BLDC motor using PI controller

B) Speed response of BLDC motor using PD controller

The speed response of BLDC motor using PD controller is given in fig. 4.11. Here, the

value of PD controller parameters Kp and Kd are tuned manually using MATLAB/Simulink.

It is observed from the figure that, the transient response is improved by using PD

controller. But some steady state error present in the speed response and does not provide

desired level of speed. Here, the speed level is found 962.7rpm, peak time is 0.0030s, rise

time is 0.0016, overshoot is 13.3214% and settling time is 0.0030s.

Fig. 4.11 Speed response of BLDC motor using PD controller

60

C) Speed response of BLDC motor using PID controller

Figure 4.12 shows the speed response of BLDC motor using PID controller. Manual tuning

is also used to determine the value of parameters (Kp, Ki and Kd) of PID controller using

MATLAB/Simulink. It is seen from the fig. 4.12 that, the steady state error as well as the

transient response is improved by using PID controller. The rise time of the speed response

is 0.0070s, overshoot is 11.2466%, settling time is 0.0411s, peak time is 0.0188s and the

speed is 1000rpm by using PID controller.

Fig. 4.12 Speed response of BLDC motor using PID controller

A comparison among the speed response of BLDC motor using these controllers is included

in table 4.2. It is observed from the data in the table that, the speed response of BLDC

motor using PID controller shows better results than other controllers.

Table 4.2 Speed response of BLDC motor using PI, PD and PID controller

Parameters PI controller PD controller PID controller

Rise time (s) 0.0034 0.0016 0.0070

Peak time (s) 0.0130 0.0030 0.0188

Settling time (s) 0.0485 0.0055 0.0411

Percentage overshoot (%OS) 18.3919 13.3214 11.2466

Speed (rpm) 1000 962.7 1000

61

D) Speed response of BLDC motor using PSO algorithm based PID controller

The PSO algorithm based PID controller is used to analyze the speed response of BLDC

motor in this work. The reference speed of the motor is set to 1000rpm. PSO algorithm

minimizes the value of the error signal iteratively and gives optimal value of PID controller

parameters. The values of error signal for 25 iterations are given in fig. 4.13. Figure 4.13

show that, the value of error signal is gradually decreasing after each iteration and become

constant.

Fig. 4.13 The values of error signal for 25 iterations

The speed response of BLDC motor using PSO algorithm based PID controller is appended

in fig. 4.14. This fig. 4.14 shows the speed response after completing 25 iterations. From the

figure, it is observed that, the BLDC motor gives desired level of speed i.e. 1000 rpm and

the overshoot of the speed response is 0.1927%. This response also shows the rise time is

0.0047s, peak time is 0.0139s and settling time is 0.0096s. On the other side, PID controller

based BLDC motor provides the rise time of the speed response is 0.0070s, peak time is

0.0188s, overshot is 11.2466% and settled at 0.0411s. It is shown in fig. 4.12. The PSO

algorithm based optimized PID controller gives better speed responses than PID controller.

62

Fig. 4.14 Speed response of BLDC motor using PSO algorithm based PID controller

After each iteration, the responses of various speed parameters such as rise time, peak time,

settling time and percentage overshoot of BLDC motor are analyzed and the responses of

some iteration are mentioned in table 4.3.

Table 4.3 Speed response of BLDC motor using PSO algorithm based PID controller

Sl. No. No. of

iteration

Rise time

(s)

Peak time

(s)

Settling time

(s)

Percentage

overshoot (%OS)

01 01 0.0047 0.0137 0.0096 0.2356

02 03 0.0047 0.0137 0.0096 0.2169

03 06 0.0047 0.0139 0.0096 0.2074

04 09 0.0047 0.0139 0.0096 0.2038

05 12 0.0047 0.0139 0.0096 0.1832

06 15 0.0047 0.0139 0.0096 0.1830

07 18 0.0047 0.0139 0.0096 0.1927

08 20 0.0047 0.0139 0.0096 0.1927

09 25 0.0047 0.0139 0.0096 0.1927

63

From the data shown in the table 4.3 it is observed that, the speed response parameters of

BLDC motor are significantly improved in each iteration. It is also observed that, it shows

the same result of speed responses after 16 number iteration.

To make comparison the speed response parameters such as rise time, settling time, peak

time and percentage overshoot of BLDC motor without using any controller, with PID

controller and with PSO algorithm based PID controller are tabulated as table 4.4 given

below.

Table 4.4 Comparison among the speed response of BLDC motor without controller, with

PID controller and with PSO algorithm based PID controller

Parameters Without

controller

With

PID controller

With PSO based

PID controller

Rise time (s) 0.0038 0.0070 0.0047

Peak time (s) 0.0088 0.0188 0.0139

Settling time (s) 0.1043 0.0411 0.0096

Percentage overshoot

(% OS)

50.46 11.2466 0.1927

Speed (rpm) 258.4 1000 1000

The optimized value of PID controller parameters (Kp, Ki and Kd) using PSO algorithm for

the system is shown below in table 4.5.

Table 4.5 Optimized value of PID controller parameters using PSO algorithm

Kp Ki Kd

6.300 230.953 0.064

4.9 Conclusion

The PSO algorithm based PID controller is developed for the system (solar MPPT based

three phase IBC with coupled inductor for BLDC motor) in this chapter to achieve

improved speed response of BLDC motor. To control the system and get desired result,

different controllers such as PI, PD and PID are used. The simulation of the system and its

performances are analyzed through MATLAB/Simulink. From the simulation results it is

64

seen that, the speed response of BLDC motor using PSO based PID controller is 1000 rpm,

rise time is 0.0047s, peak time is 0.0139s, the percentage overshoot is 0.1927% and settling

time is 0.0064s. Besides, the BLDC motor using PID controller provides speed of 1000rpm

where, the rise time of the speed response is 0.0070s, peak time is 0.0188s, overshoot is

11.2466% and settling time is 0.0411s. It is observed from the above analysis that, the

system gives superior speed response of BLDC motor by using PSO based PID controller.

65

CHAPTER 5

Conclusion and Future Works

5.1 Conclusion

Solar PV system has recently attracted much attention due to its inherent advantages. In

order to utilize the solar energy effectively, an efficient MPPT technique and a suitable DC-

DC converter are greatly essential. IBC plays an important role as a suitable interface in

various places.

This thesis presents the performance of BLDC motor fed from solar energy using three

phase IBC with coupled inductor. Incremental conductance MPPT algorithm is used to

control the converter. The overall system performance has been analyzed using

MATLB/Simulink. Solar MPPT based boost converter, two phase IBC, three phase IBC and

three phase IBC using coupled inductor are analyzed in term of voltage and current ripples.

It is found from the simulation results that, three phase IBC using coupled inductor shows

lower amplitude of ripples compared to other converters. It shows input current ripple of

0.13A, output current ripple of 0.18A and output voltage ripple of 0.87V.

Three phase IBC and three phase IBC with coupled inductor are used to analyze the

performance such as stator current, speed and torque of BLDC motor. The output results of

three phase IBC with coupled inductor fed BLDC motor are more smoother than three

phase IBC fed BLDC motor. From the simulation results, the starting current of coupled

inductor three phase IBC fed BLDC motor is 18.8A and settled at 0.1033s, speed is

258.4rpm which is settled at 0.1043s and torque ripple is 1.41Nm. On the other hand, the

speed response of aforesaid motor is rise time of 0.0038s, peak time of 0.0088s, settling

time of 0.1043s and peak overshoot of 50.46%.

To control and enhance the speed response of three phase IBC with coupled inductor fed

BLDC motor, PID controller gives better performance in comparison with PI and PD

controller. PSO algorithm is used in order to tune the parameters of PID controller

optimally. The BLDC motor provides optimum speed response while using PSO algorithm

based PID controller. Speed of 1000 rpm, rise time of 0.0047s, peak time of 0.0139s,

percentage overshoot of 0.1927% and settling time of 0.0096s are found from the

simulation results.

66

From the simulation results it is observed that, three phase IBC with coupled inductor has

been proved to be potential interface as compared to boost converter, two phase and three

phase IBC for reducing current and voltage ripples and improving the performance such as

stator current, speed and torque of BLDC motor. Three phase IBC using coupled inductor

fed BLDC motor along with PSO based PID controller shows superior performance than

other controller in term of speed response parameters of BLDC motor such as rise time,

peak time, settling time and percentage overshoot.

5.2 Future Works

In this thesis, the performance of BLDC motor using interleaved boost converter with

coupled inductor is analyzed which is fed from MPPT based solar power and PSO based

PID controller is used to improve the speed response of BLDC motor. Here, the

performance analysis of the system is done through simulation software. In future, this

system can be implemented practically to investigate its actual potential. It may also include

performance analysis of the practical system by comparing with the simulated one.

The optimization algorithm used in this system is PSO algorithm. In future, the speed

responses of the BLDC motor can be further optimized by using other popular optimization

algorithms like genetic algorithm (GA) and the combination of GA-PSO algorithm. By

using these algorithms, the speed responses of the BLDC motor can be compared to obtain

better result and to find the best optimization algorithm for this system among these

algorithms.

The switching losses in the switches of the converter are not considered in this thesis.

Future research can be done to determine the switching losses of the switching devices.

Moreover, the soft switching techniques can be applied to reduce switching losses and

improve more efficiency of the converter.

67

References

[1] M. Lasheen, A. K. A. Rahman, M. A. Salam and S. Ookawara, “Adaptive reference

voltage-based MPPT technique for PV applications”, IET Renewable Power

Generation, vol. 11, no. 5, pp. 715-722, January 2017.

[2] C. C. Hua, Y. H. Fang and C. J. Wong, “Improved solar system with maximum

power point tracking”, IET Renewable Power Generation, vol. 12, no. 7, pp. 806-

814, May 2018.

[3] O. Ezinwanne, F. Zhongwen and L. Zhijun, “Energy performance and cost

comparison of MPPT techniques for photovoltaics and other applications”, Energy

Procedia (Elsevier), vol. 107, pp. 297-303, February 2017.

[4] P. K. Vineeth Kumar and K. Manjunath, “A comparitive analysis of MPPT

algorithms for solar photovoltaic systems to improve the tracking accuracy”,

International Conference on Control, Power, Communication and Computing

Technologies (ICCPCCT), IEEE conference, pp. 540-547, Kannur, India, March

2018.

[5] M. Z. Hossain, N. A. Rahim and J. a. Selvaraja, “Recent progress and development

on power DC-DC converter topology, control, design and applications: A review”,

Renewable and Sustainable Energy Reviews, (Elsevier) vol. 81, part 1, pp. 205-230,

January 2018.

[6] S. J. Chen, S. P. Yang, C. M. Huang and C. K. Lin, “Interleaved high step-up DC-DC

converter with parallel-input series-output configuration and voltage multiplier

module”, IEEE International Conference on Industrial Technology (ICIT), pp. 119-

124, Toronto, Canada, March 2017.

[7] M. Shaneh, M. Niroomand and E. Adib, “Ultra high-step up non-isolated interleaved

boost converter”, IEEE Journal of Emerging and Selected Topics in Power

Electronics, Early Access, December 2018.

[8] A. Naderi and K. Abbaszadeh, “High step-up DC–DC converter with input current

ripple cancellation”, IET Power Electronics, vol. 9, no. 12, pp. 1755-4535, May

2016.

[9] E. Babaei, M. E. S. Mahmoodieh and H. M. Mahery, “Operational modes and output-

voltage-ripple analysis and design considerations of buck–boost DC–DC converters”,

IEEE Transactions On Industrial Electronics, vol. 59, no. 1, pp. 381-391, January

2012.

68

[10] Ritu, S. Mishra, N. Verma and S. D. Shukla, “Implementation of solar based PWM

fed two phase interleaved boost converter”, International Conference on

Communication, Control and Intelligent Systems (CCIS), IEEE conference, pp. 470-

476, Mathura, India 2015.

[11] B. Akhlaghi and H. Farzanehfard, “Family of ZVT Interleaved Converters With Low

Number of Components”, IEEE Transactions on Industrial Electronics, vol 65, no.

11, pp. 8565-8573, November 2018.

[12] S. Banerjee, A. Ghosh and N. Rana, “An improved interleaved boost converter with

PSO-based optimal type-III controller”, IEEE Journal of Emerging and Selected

Topics in Power Electronics, vol. 5, no. 1, pp. 323-337, March 2017.

[13] B. C. Barry, J. G. Hayes, M. S. Rylko, R. Stala, A. Penczek, A. Mondzik and R. T.

Ryan, “Small-signal model of the two-phase interleaved coupled-inductor boost

converter”, IEEE Transactions on Power Electronics, vol. 33, no. 9, pp. 8052-8064,

September 2018.

[14] R. Kumar and B. Singh, “BLDC motor driven solar PV array fed water pumping

system employing Zeta converter”, IEEE Transactions on Industry Applications, vol.

52, no. 3,pp. 2315-2322, May 2016.

[15] M. P. Maharajan and S. A. E. Xavier, “Design of speed control and reduction of

torque ripple factor in BLDC motor using spider based controller”, IEEE

Transactions on Power Electronics, vol. 34, no. 8, pp. 7826-7837, November 2018.

[16] G. Jiang, C. Xia, W. Chen, T. Shi, X. Li and Y. Cao, “Commutation torque ripple

suppression strategy for brushless dc motors with a novel non-inductive boost front

end”, IEEE Transactions on Power Electronics, vol. 33, no. 5, pp. 4274-4284, May

2018.

[17] B. K. Baby and S. George, “Torque ripple reduction in BLDC motor with 120 degree

conduction inverter”, Annual IEEE India Conference (INDICON), IEEE conference,

pp. 1116-1121, Kochi, India, December 2012.

[18] V. Viswanathan and S. Jeevananthan, “Hybrid converter topology for reducing

torque ripple of BLDC motor”, IET Power Electronics, vol. 10, no. 12, pp. 1572-

1587, October 2017.

[19] K. Premkumara and B.V. Manikandan, “Speed control of Brushless DC motor using

bat algorithm optimized Adaptive Neuro-Fuzzy Inference System” Applied Soft

Computing (Elsevier), vol. 32, pp. 403-419, July 2015.

[20] S. K. Das, D. Verma, S. Nema and R.K. Nema, “Shading mitigation techniques:

69

State-of-the-art in photovoltaic applications”, Renewable and Sustainable Energy

Reviews (Elsevier), vol. 78, pp. 369-390, October 2017.

[21] R. Blange, C. Mahanta and A. K. Gogoi, “MPPT of solar photovoltaic cell using

Perturb & Observe and Fuzzy Logic controller algorithm for buck-boost DC-DC

converter”, International Conference on Energy, Power and Environment: Towards

Sustainable Growth (ICEPE), IEEE conference, Shillong, India, June 2015.

[22] D.S.G. Krishna and M. Ravali, “An intelligent MPPT controller for a PV source

using cascaded artificial neural network controlled DC link”, International

Conference on Signal Processing, Communication, Power and Embedded System

(SCOPES), IEEE conference, pp. 983-988, Paralakhemundi, India, October 2016.

[23] M.A. Danandeh and S.M. Mousavi G., “Comparative and comprehensive review of

maximum power point tracking methods for PV cells”, Renewable and Sustainable

Energy Reviews (Elsevier), vol. 82, pp. 2743-2767, February 2018.

[24] P. Mohanty, G. Bhuvaneswari, R. Balasubramanian and N. K. Dhaliwal, “MATLAB

based modeling to study the performance of different MPPT techniques used for

solar PV system under various operating conditions”, Renewable and Sustainable

Energy Reviews, vol. 38, pp. 581–593, October 2014.

[25] A. Satif, L. Hlou and R. Elgouri “An improved perturb and observe maximum power

point tracking algorithm for photovoltaic systems”, Renewable Energies, Power

Systems & Green Inclusive Economy (REPS-GIE), IEEE conference, Casablanca,

Morocco, April 2018.

[26] J. Ahmed and Z. Salam “A modified P&O maximum power point tracking method

with reduced steady state oscillation and improved tracking efficiency”, IEEE

Transactions on Sustainable Energy, vol. 7, no.4, pp. 1506-1515, October 2016.

[27] K. Jain, M. Gupta and A. K. Bohre, “Implementation and comparative analysis of

P&O and INC MPPT method for PV system”, 8th IEEE India International

Conference on Power Electronics (IICPE), JAIPUR, India, December 2018.

[28] N. Barua, A. Dutta, S. Chakma, A. Das and S. S. Chowdhury, “Implementation of

cost-effective MPPT solar photovoltaic system based on the comparison

between Incremental Conductance and P&O algorithm”, IEEE International WIE

Conference on Electrical and Computer Engineering (WIECON-ECE), pp. 143-146,

Pune, India, December 2016.

[29] H. Bahrami, E. Adib, S. Farhangi, H. I. Eini and R. Golmohammadi, “ZCS-PWM

interleaved boost converter using resonance-clamp auxiliary circuit”, IET Power

70

Electronics, vol. 10, no. 3, pp. 405-412, March 2017.

[30] M. V. Naik and P. Samuel “Analysis of ripple current, power losses and high

efficiency of DC–DC converters for fuel cell power generating systems”, Renewable

and Sustainable Energy Reviews (Elsevier), vol. 59, pp. 1080-1088, June 2016.

[31] M. Esteki, E. Adib, H. Farzanehfard and S. A. Arshadi “Auxiliary circuit for zero-

voltage-transition interleaved pulse-width modulation buck converter”, IET Power

Electronics, vol 9, no. 3, pp. 568-575, March 2016.

[32] D. Wu, G. C. Lopez and A. J. Forsyth, “Discontinuous conduction/current mode

analysis of dual interleaved buck and boost converters with interphase transformer”,

IET Power Electronics, vol. 9, no. 1, pp. 31-41, January 2016.

[33] J. S. Alagesan, J. Gnanavadivel, N. S. Kumar and K. S. K. Veni, “Design and

simulation of fuzzy based DC-DC interleaved Zeta converter for photovoltaic

applications”, 2nd International Conference on Trends in Electronics and Informatics

(ICOEI), IEEE conference, pp. 704-709, Tirunelveli, India, May 2018.

[34] C. Shi, A. Khaligh and H. Wang, “Interleaved SEPIC power factor pre-regulator

using coupled inductors in discontinuous conduction mode with wide output

voltage”, IEEE Transactions on Industry Applications, vol. 52, no. 4, pp. 3461-3471,

August 2016.

[35] K. D. Joseph, A. E. Daniel and A. Unnikrishnan, “Interleaved Cuk converter with

improved transient performance and reduced current ripple”, IET The Journal of

Engineering, vol. 2017, no. 7, pp. 362-369 July 2017.

[36] N. Rana, M. Kumar, A. Ghosh and S. Banerjee “A novel interleaved tri-state boost

converter with lower ripple and improved dynamic”, IEEE Transactions on Industrial

Electronics, vol. 65, no. 7, pp. 5456-5465, July 2018.

[37] Y. T. Chen, Z. M. Li and R. H. Liang, “A novel soft-switching interleaved coupled-

inductor boost converter with only single auxiliary circuit”, IEEE transactions on

power electronics, vol. 33, no. 3, pp 2267-2281, March 2018.

[38] W. Martinez, C. A. Cortes, J. Imaoka, K. Umetani and M. Yamamoto, “Current

ripple modeling of an interleaved high step-up converter with coupled inductor”,

IEEE 3rd International Future Energy Electronics Conference and ECCE Asia

(IFEEC 2017 - ECCE Asia), pp. 1084-1089, Kaohsiung, Taiwan, June 2017.

[39] M.R. Ahmed, R. Todd and A. J. Forsyth, “Soft-switching operation of the dual

interleaved boost converter over all duty ratios”, IET Power Electronics, vol. 10, no.

11, September 2017.

71

[40] S. S. Mohammed, D. Devaraj, “Simulation of incremental conductance MPPT based

two phase interleaved boost converter using MATLAB/Simulink”, IEEE

International Conference on Electrical, Computer and Communication

Technologies (ICECCT), pp. 1-6, Coimbatore, India 2015.

[41] N. Selvaraju, P. Shanmugham and S. Somkun, “Two-phase interleaved boost

converter using coupled inductor for fuel cell applications”, Energy Procedia

(Elsevier), vol. 138, pp. 199-204, October 2017.

[42] A. Silven, J. T. Kuncheria, “Performance improvement of a BLDC motor using

interleaved buck converter”, International Electronics Symposium (IES), IEEE

conference, pp. 244-247, Denpasar, Indonesia, September 2016.

[43] N. H. A. Khanipah, M. Azri, M. H. N. Talib, Z. Ibrahim and N. A. Rahim,

“Interleaved boost converter for fuel cell application with constant voltage

technique”, IEEE Conference on Energy Conversion (CENCON), pp. 55-60, Kuala

Lumpur, Malaysia, October 2017.

[44] A. G. de Castro, W. C. A. Pereira, T. E. P. Almeida, C. M. R. de Oliveira, J. R. B. A.

Monteiro and A. A. de Oliveira Jr., “Improved finite control-set model-based direct

power control of BLDC motor with reduced torque ripple”, IEEE Transactions on

Industry Applications, vol. 54, no. 5, pp. 4476-4484, October 2018.

[45] I. Anshory, I. Robandi and Wirawan “Monitoring and optimization of speed settings

for brushless direct current (BLDC) Using particle swarm optimization (PSO)”, IEEE

Region 10 Symposium (TENSYMP), pp. 243-248, Bali, Indonesia, May 2016.

[46] F. Slah, A. Mansour, M. Hajer and B. Faouzi, “Analysis, modeling and

implementation of an interleaved boost DC-DC converter for fuel cell used in electric

vehicle”, International Journal of Hydrogen Energy (Elsevier), vol. 42, no. 48, pp.

28852-28864, November 2017.

[47] G. Y. Chen and J. W. Perng “PI Speed Controller Design based on GA with Time

Delay for BLDC Motor using DSP”, IEEE International Conference on Mechatronics

and Automation (ICMA), IEEE conference, pp. 1174-1179, Takamatsu, Japan,

August 2017.

[48] S. Bencharef and H. Boubertakh, “Optimal Tuning of a PD Control by Bat Algorithm

to Stabilize a Quadrotor”, 8th International Conference on Modelling, Identification

and Control (ICMIC-2016), IEEE conference, pp. 938-942, Algiers, Algeria,

November 2016.

75

[49] A. Varshney, D. Gupta, B. Dwivedi, “Speed response of brushless DC motor using

fuzzy PID controller under varying load condition”, Journal of Electrical Systems

and Information Technology (Elsevier), vol. 4, no. 2, pp. 310-321, September 2017.

[50] A. Zemmit, S. Messalti and A. Harrag, “A new improved DTC of doubly fed

induction machine using GA-based PI controller”, Ain Shams Engineering Journal

(Elsevier), vol. 9, no. 4, pp. 1877–1885, December 2018.

[51] K. K. Nimisha and R. Senthilkumar “Optimal tuning of PID controller for switched

reluctance motor speed control using particle swarm optimization”, International

Conference on Control, Power, Communication and Computing Technologies

(ICCPCCT), IEEE conference, pp. 487-491, Kannur, India, March 2018.

[52] M. Bernard and P. Musilek, “Ant-based optimal tuning of PID controllers for load

frequency control in power systems”, IEEE Electrical Power and Energy Conference

(EPEC), pp. 1-6, Saskatoon, Canada, October 2017.

[53] M. K. Merugumalla and P. K. Navuri “PSO and Firefly Algorithms based control of

BLDC motor drive”, 2nd International Conference on Inventive Systems and Control

(ICISC), IEEE conference, pp. 994-999, Coimbatore, India, January 2018.

[54] G. R. Kanagachidambaresan, R. Anand and A. Kalam, “Perturb and Observe (P&O)

based MPPT controller for PV connected brushless DC motor drive”, International

conference on Electrical, Instrumental and Communication Engineering

(ICEICE2017), IEEE conference, pp. 1-5, Karur, India, 2017.

[55] M. Ridwan, D. C. Riawan and H. Suryoatmojo, “Particle swarm optimization-based

BLDC motor speed controller with response speed consideration”, International

Seminar on Intelligent Technology and Its Applications (ISITIA), IEEE conference,

pp. 193-198, Surabaya, Indonesia, August 2017.

[56] H. Liu and D. Zhang “Two-phase interleaved inverse-coupled inductor boost without

right half plane zeros”, IEEE Transactions on Power Electronics, vol. 32, no. 3, pp.

1844-1859, March 2017.

[57] R. Karthikeyan and GN. S. Amreiss “PV based interleaved boost converter for

pumping applications”, International Conference on Intelligent and Advanced

System (ICIAS), IEEE conference, Kuala Lumpur, Malaysia, August 2018.

[58] J. P. A. Angulo, J. C. R. Caro, E. H. Sandoval, A. V. Gonzalez, J. C. M. Maldonado

and J. E. V. Resendiz, “Input current ripple cancelation by interleaving boost and

Cuk DC-DC converter”, International Conference on Electronics, Communications

and Computers (CONIELECOMP), IEEE conference, pp. 133-138, Cholula, Mexico,

76

February 2018.

[59] G. R. C. Mouli, J. Schijffelen, P. Bauer and M. Zeman, “Design and comparison of a

10kW interleaved boost converter for PV application using Si and SiC devices”,

IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 5 , no. 2,

pp. 610-623, June 2017.

[60] K. J. Reddy and N. Sudhakar, “High voltage gain interleaved boost converter with

neural network based MPPT controller for fuel cell based electric vehicle

applications”, IEEE Access, vol. 6, pp. 3899-3908, January 2018.

[61] F. Slah, A. Mansour, M. Hajer and B. Faouzi, “Analysis, modeling and

implementation of an interleaved boost DC-DC converter for fuel cell used in electric

vehicle”, International Journal of Hydrogen Energy (Elsevier), vol. 42, no. 48, pp.

28852-28864, November 2017.

[62] H. Xu, D. Chen, F. Xue and X. Li “Optimal design method of interleaved boost PFC

for improving efficiency from switching frequency, boost inductor and output

voltage”, IEEE Transactions on Power Electronics, vol. 34, no. 7, pp. 6088-6107,

July 2019.

[63] S. Prabhakar and J. L. F. Daya, “A comparative study on the performance of

interleaved converters for ev battery charging”, IEEE 6th International Conference

on Power Systems (ICPS), New Delhi, India, March 2016.

[64] C. Chang and M. A. Knights, “Interleaving technique in distributed power

conversion systems”, IEEE Transactions on Circuits and Systems I: Fundamental

Theory and Applications, vol. 42, no. 5, pp. 245-251, May 1995.

[65] D.Y. Jung, Y. H. Ji, S. H. Park, Y. C. Jung and C. Y. Won, “Interleaved soft-

switching boost converter for photovoltaic power-generation system” IEEE

Transactions on Power Electronics, vol. 26, no. 4, pp. 1137-1145, April 2011.

[66] S. Sakulchotruangdet and S. Khwan-on “Three-phase interleaved boost converter

with fault tolerant control strategy for renewable energy system applications”,

Procedia Computer Science (Elsevier), vol. 86, pp. 353 356, 2016.

[67] M. Ünlü, O. Kircioğlu, S. Çamur, “Two-phase interleaved SEPIC MPPT using

coupled inductors in continuous conduction mode”, 18th International Power

Electronics and Motion Control Conference (PEMC), IEEE conference, pp. 216-220,

Budapest, Hungary, August 2018.

[68] P. W. Lee, Y. S. Lee, D.K.W. Cheng and X. C. Liu, “Steady-state analysis of an

interleaved boost converter with coupled inductors”, IEEE Transactions On

77

Industrial Electronics, vol. 47, no. 4, pp. 787-795, August 2000.

[69] M. H. Rashid, “Power electronics, circuits, devices and applications”, 3rd edition.

[70] M. A. A. M. Zainuri, M. A. M. Radzi, A. C. Soh and N. A. Rahim, “Development of

adaptive perturb and observe-fuzzy control maximum power point tracking for

photovoltaic boost DC–DC converter”, IET Renewable Power Generation, vol. 8, no.

2, pp. 183-194, March 2014.

[71] H. Bounechba, A. Bouzid, H. Snani and Abderrazak Lashab, “Real time simulation

of MPPT algorithms for PV energy system”, International Journal of Electrical

Power & Energy Systems (Elsevier), vol. 83, pp. 67-78, December 2016.

[72] A. Thangavelu, S. Vairakannu and D. Parvathyshankar, “Linear open circuit voltage-

variable step-size incremental conductance strategy-based hybrid MPPT controller

for remote power applications”, IET Power Electronics, vol. 10, no. 11, September

2017.

[73] A. A. Zadeh, M. Toulabi, A. S. Dobakhshari, S. T. Broujeni, A. M. Ranjbar, “A

novel technique to extract the maximum power of photovoltaic array in partial

shading conditions”, International Journal of Electrical Power & Energy Systems

(Elsevier), vol. 101, pp. 500-512, October 2018.

[74] D. Canny and F. Yusivar, “Maximum power point tracking (MPPT) algorithm

simulation based on fuzzy logic controller on solar cell with boost converter”, 2nd

International Conference on Smart Grid and Smart Cities (ICSGSC), IEEE

conference, pp. 117-121, Kuala Lumpur, Malaysia, August 2018.

[75] Solarex MSX60 and MSX64 Datasheet. Available online:

https://www.solarelectricsupply.com/solarex-msx-60-w-junction-box-551 (accessed

on 21 November, 2019)

[76] J. Cai, C. Lai and N. C. Kar, “Modeling and analysis of torque ripple in a brushless

dc motor considering spatial harmonics”, IEEE 30th Canadian Conference on

Electrical and Computer Engineering (CCECE), Windsor, Canada, May 2017.

[77] P. Krause, “Analysis of Electric Machinery”, New York, USA: McGraw-Hill, 1986.

[78] M. S. Boroujeni, G. R. A. Markadeh, J. Soltani, “Torque ripple reduction of

brushless DC motor based on adaptive input-output feedback linearization”, ISA

Transactions (Elsevier), vol. 70, pp. 502-511, September 2017.

[79] S. Baldursson, “BLDC motor modelling and control – A Matlab/Simulink

implementation”, Master thesis in Electric Power Engineering, Chalmers University

Of Technology, Gothenburg, Sweden, May 2005.

78

[80] P. Awari, P. Sawarkar, R. Agarwal, A. Khergade and S. Bodkhe, “Speed control and

electrical braking of axial flux BLDC motor”, 6th International Conference on

Computer Applications In Electrical Engineering-Recent Advances (CERA), IEEE

conference, pp. 297-302, Roorkee, India, October 2017.

[81] M. A. Islam, M. B. Hossen, B. Banik and B. C. Ghosh, “Field oriented space vector

pulse width modulation control of permanent magnet brushless DC motor”, IEEE

Region 10 Humanitarian Technology Conference (R10-HTC), pp. 322-327, Dhaka,

Bangladesh, December 2017.

[82] R. Krishnan, “Electric Motor Drives: Modeling Analysis and Control”, Upper Saddle

River, NJ, USA: Prentice-Hall, 2001.

[83] H. S. Hameed, “Brushless DC motor controller design using MATLAB

applications”, 1st International Scientific Conference of Engineering Sciences - 3rd

Scientific Conference of Engineering Science (ISCES), IEEE conference, pp. 44-49,

January 2018.

[84] K. S. Devi, R. Dhanasekaran and S.Muthulakshmi “Improvement of speed control

performance in bldc motor using fuzzy PID controller”, International Conference on

Advanced Communication Control and Computing Technologies (ICACCCT), IEEE

conference, pp. 380-384, Ramanathapuram, India, May 2016.

[85] S. Jittapramualboon and W. Assawinchaichote, “Optimization of PID controller

based on taguchi combined particle swarm optimization for AVR system of

synchronous generator”, International Computer Science and Engineering

Conference (ICSEC), IEEE conference, Chiang Mai, Thailand, December 2016.

[86] S. Agrawal, V. Kumar, K. P. S. Rana and P. Mishra, “Optimization of PID controller

with first order noise filter”, International Conference on Futuristic Trends on

Computational Analysis and Knowledge Management (ABLAZE), IEEE conference,

pp. 226-231, Noida, India, February 2015.

[87] Y.-J. Gong, J.-J. Li, Y. Zhou, Y. Li, H. S.-H. Chung, Y.-H. Shi and Jun Zhang,

“Genetic learning particle swarm optimization”, IEEE Transactions On Cybernetics,

vol. 46, no. 10, pp. 2277-2290, October 2016.

[88] Z. Abdmouleh, A. Gastli, L. B.-Brahim, M. Haouari and N. A. Al-Emadi, “Review

of optimization techniques applied for the integration of distributed generation from

renewable energy sources”, Renewable Energy (Elsevier), vol. 113, pp. 266-280,

December 2017.

[89] M. S. Zaky, “A self-tuning PI controller for the speed control of electrical motor

79

drives”, Electric Power Systems Research (Elsevier), vol. 119, pp. 293-303, February

2015.

[90] K. Ogata, “Modern Control Engineering”, 4th edition, New Jersey, USA: Prentice

Hall, 2002.

[91] K. Premkumar and B. V. Manikandan, “Bat algorithm optimized fuzzy PD based

speed controller for brushless direct current motor”, Engineering Science and

Technology, an International Journal (Elsevier), vol. 19, no. 2, pp 818-840, June

2016.

[92] K. J. Åström and T. Hägglund, PID Controllers: Theory Design and Tuning,

Research Triangle Park, NC, USA: Instrument Society of America, 2nd

Edition, 1995.

[93] K. H. Ang, G. Chong and Y. Li, “PID control system analysis, design, and

technology”, IEEE Transactions On Control Systems Technology, vol. 13, no. 4, pp.

559-576, July 2005.

[94] J. Kennedy and R. Eberhart, “Particle swarm optimization”, Proceedings of ICNN'95

- International Conference on Neural Networks, pp. 1942-1948, WA, Australia,

December 1995.

[95] Z. L. Gaing, “A particle swarm optimization approach for optimum design of PID

controller in AVR system”, IEEE Transactions on Energy Conversion, vol. 19, no. 2,

pp. 384-391, June 2004.

[96] M. H. T. Omar, W. M. Ali and M. Z. Mostafa, “PID controller using swarm

intelligence”, International Review of Automatic Control, vol. 3, no. 4, pp.139- 147,

May 2010.

[97] M. Calvini, M. Carpita, A. Formentini and M. Marchesoni, “PSO-based self-

commissioning of electrical motor drives”, IEEE Transactions on Industrial

Electronics, vol. 62, no. 2, pp. 768-776, February 2015.

[98] M. M. R. A. Milani, T. Cavdar and V. F. Aghjehkand, “Particle swarm optimization -

based determination of ziegler nichols parameters for PID controller of brushless dc

motors”, International Symposium on Innovations in Intelligent Systems and

Applications, IEEE conference, Trabzon, Turkey, July 2012.

[99] D. Wu, N. Jiang, W. Du, K. Tang and X. Cao, “Particle swarm optimization with

moving particles on scale-free networks”, IEEE Transactions on Network Science

and Engineering, Early Access, July 2018.

80

Publications

[1] S. Nahar, M. B. Uddin, “Analysis the performance of interleaved boost converter”,

4th International Conference on Electrical Engineering and Information &

Communication Technology (iCEEiCT), IEEE conference, pp. 547-551, Dhaka,

Bangladesh, September 2018.

[2] S. Nahar, M. R. Ahmed, A. Afrin, “Performance analysis of solar MPPT (InC

algorithm) based three phase interleaved boost converter using coupled inductor for

brushless DC motor”, 2018 International Conference on Advancement in Electrical

and Electronic Engineering (ICAEEE), IEEE conference, Gazipur, Bangladesh,

November 2018.

81

Appendix

MATLAB code of PSO algorithm for tuning PID controller to improve

the speed response parameters of BLDC motor

% Particle-Swarm Optimization Algorithm

clc

clear all

close all

%Initializing variables

popsize=5; % Size of the swarm

npar= 3; % number of PID parameters

maxit = 25; % Maximum number of iterations

c1 = 2; % cognitive parameter

c2 = 2; % social parameter

w=0.11; % inertia weight

P1=[1 10 20 30 50];

I1=[100 110 120 130 140];

D1=[0.001 0.003 0.006 0.009 0.0001];

A=[P1' I1' D1'];

par=A;

vel=zeros(popsize,npar);

for l=1:popsize

P=par(l,1);

I=par(l,2);

D=par(l,3);

sim('pidpso');

cost(l)=sum(e.^2);

end

localpar = par; % location of local minima

localcost = cost; % cost of local minima

82

% Finding best particle in initial population

[globalcost,indx] = min(cost);

globalpar=par(indx,:);

%Start iterations

iter = 0; % counter

while iter < maxit

iter = iter + 1;

for l=1:popsize

P=par(l,1);

I=par(l,2);

D=par(l,3);

sim('pidpso');

cost(l)=sum(e.^2);

end

r1 = rand(popsize,npar); % random numbers

r2 = rand(popsize,npar); % random numbers

for i=1:popsize

if cost(i)<localcost(i)

localcost(i)=cost(i);

for j=1:npar

localpar(i,j)=par(i,j);

end

end

end

[mincost,index] = min(cost);

if mincost<globalcost

globalcost=mincost;

globalpar=par(index,:);

end

% update velocity = vel

% update position = par

for i=1:popsize

for j=1:npar

vel(i,j)=(w*vel(i,j))+c1*r1(i,j)*(localpar(i,j)-par(i,j))+c2*r2(i,j)*(globalpar(j)-par(i,j));

par(i,j)=par(i,j)+vel(i,j);

83

end

end

minc(iter+1)=min(cost); % minimum cost for this iteration

globalmin(iter)=globalcost; % best minimum cost so far

meanc(iter+1)=mean(cost); % average cost

P=globalpar(1);

I=globalpar(2);

D=globalpar(3);

sim('pidpso');

yy(iter,:)=s;

end

figure

iters=1:length(minc)-1;

plot(iters,globalmin,'k');

xlabel('Generation');ylabel('Cost function');

figure

plot(t,yy(25,:),'b');