an efficient solar / wind/ battery71)_naandhini journal_(p.430-440).pdfbidirectional power flow is...

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Int. J. Engg. Res. & Sci. & Tech. 2015 L Nagarajan and S Nandhini, 2015

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AN EFFICIENT SOLAR / WIND/ BATTERY

HYBRID SYSTEM WITH HIGH POWER

CONVERTER USING PSO

L Nagarajan1* and S Nandhini1

This project presents a method for the optimization of the power generated from a HybridRenewable Energy Systems (HRES) in order to achieve the load of typical house as example ofload demand. Particle Swarm Optimization Technique (PSO) is used as optimization searchingalgorithm due to its advantages over the other. The hybrid system consists of photovoltaic panels,wind turbines and storage batteries. The wind and PV are used as main energy sources, whilethe battery is used as back-up energy source. Two individual DC-DC boost converters are usedto control the power flow to the load. A simple and cost effective control with DC-DC converteris used for maximum power point tracking (MPPT) and hence maximum power is extractedfrom the wind turbine and the photo voltaic array. The objectives are reducing converter losses,improving efficiency of the hybrid system, and to maintaining voltage stability of the system. Themodeling of hybrid system is developed in MATLAB-SIMULINK. As well as to improve the reliabilitycost of conversion optimization problems, Improved optimization algorithms, PSO are used tosolve nonlinear hybrid analysis is any integer optimization problem. on the basis of PSO algorithmstandard techniques then there is the first step convergence factor is applied to improve thedetection performance of both migration are used to improve the ability of the algorithm to findthe best in the whole world.

Keywords: Hybrid wind-photovoltaic system, Cost minimization, Particle swarm optimization,DC-DC converter, dual active bridge (DAB)

*Corresponding Author: L Nagarajan � [email protected]

1 Engg. Annai Mathammal Sheela Engineering College, Namakkal, India.

Int. J. Engg. Res. & Sci. & Tech. 2015

Research Paper

INTRODUCTION

Global warming and climatic shift has become a

major concern now-a-days. Because of this, most

of the countries have begun to turn their attention

towards the clean green renewable energy

sources. This is currently widely used which

poses a bright future for the worlds energy needs.

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Many researchers have started to focus on this

area as these are sustainable and convenient

alternatives. This analysis provides the main ideas

for generation of the active and reactive power

references.

The unbalance created by asymmetrical loads

is analyzed, with the mathematical symmetric

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decomposition theory. An unbalanced three-

phase system can be decomposed into three

balanced symmetric three-phase systems. By

using an oscillating stator active reference,

calculated from the power and torque estimation,

it is possible to eliminate the torque oscillations

and ensure sinusoidal stator currents exchange,

thus avoiding the necessity to calculate the

positive- and negative-sequence components

(Yang B et al., 2010). The operation of the DAB

dc–dc converter has been verified through

extensive simulations. A DAB converter prototype

was designed on the basis of the proposed model

and was built for an aerospace energy storage

application. The square-wave operating mode of

DAB is the best mode for high-power transfer.

The proposed model produced key design

equations for the square-wave mode of the DAB

dc–dc converter. These equations are useful in

predicting losses that occur in the devices and

passive components and enable a study of the

converter characteristics, in addition to aiding in

the practical design of converter prototypes

(Kellogg W D et al., 1998).

For a typical PV array, the output voltage is

relatively low, and a high voltage gain is obligatory

to realize the grid-connected function.

Accordingly, an accurate steady-state model is

obtained and verified by the simulation and

experimental results, and a full-bridge inverter with

bidirectional power flow is used as the second

power-processing stage, which can stabilize the

dc-bus voltage and shape the output current. The

proposed PV system employs a high step-up ZVT-

interleaved boost converter with winding-coupled

inductors and active-clamp circuits as the first

power-processing stage, and high voltage gain

is obtained by the turns ratio selection of winding-

coupled inductors (Chedid R and S Rahman,

1997). Wind turbine generators and PV panels

have their own merits for not contributing pollution

in the environment but they require high capital

and installation costs. Wind turbine generators

and PV panels have their own merits for not

contributing pollution in the environment but they

require high capital and installation costs. The

main objective of the design criteria is to minimize

the total cost which includes initial, operational

and maintenance cost. For achieving optimal

design conditions for this hybrid system particle

swarm optimization is adopted. The hybrid offers

better reliability and lower cost compared to

individual renewable sources of energy. The use

of renewable energy offers substantial

environmental credit when compared to

conventional alternatives.

The proposed analysis allows the user to study

the interaction among economic and operational

factors and hence it offers a useful tool for the

design and analysis of cost effective hybrid power

systems (Abad G et al., 2010). Installation of

experience with traditional power design and

optimization of design and operation cannot be

seen with. To solve the problem in a

comprehensive objective function to present the

objective function of the solar wind. And reliability

of the storage cells can be calculated with an

investment of erosion format system resources,

including the number of solar cells and batteries,

but the type and amount of solar wind to change.

. This analysis allows the user to study the

interaction among economic and operational

factors and hence it offers a useful tool for the

design and analysis of cost effective hybrid power

systems (Yang Bet al., 2010). To achieve a fast

and stable response for the real power control,

the intelligent controller consists of a Radial Basis

Function Network and an modified Elman Neural

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Network for maximum power point tracking. Thepitch angle of wind turbine is controlled by theModified Elman Neural Network (MENN). Anefficient power sharing technique among energysources are successfully demonstrated withmore efficiency, a better transient and morestability response, even under disturbanceconditions (Naayagi R K et al., 2012; Naveen RamG et al., 2013). The wind turbine was consideredas the only source of power in this study. Usingthis model the system response to a recordedwind gust is investigated by calculating thegenerator current, the rectifier current, the loadcurrent, the battery charging current, and thebattery voltage.

The calculated results are then verified bycomparing them with the actual values obtainedfrom the data acquisition system. The accuracyof the simplified model is demonstrated bycomparing the calculated response with recordedby a data acquisition system (Amam HossainBagdadee , 2014). An accurate steady-statemodel of the converter is obtained and verified bythe simulation and experimental results. A full-bridge inverter with bidirectional power flow isused as the second power-processing stage, tostabilize the dc-bus voltage and shape the outputcurrent. Furthermore, a simple MPPT methodbased in power balance is applied in the PVsystem and represents a good performance.Furthermore, a minimum distance between thenearest existing distribution line and the load wascalculated for each of the three configurationsthat would justify the cost of installing a standalonegenerating system as opposed to constructing aline extension and supplying the load withconventional utility power (Wilmshurst S M B,1988; Borowy B S and Salameh Z M, 1997).

The technique uses linear programming

principles to reduce the cost of electricity while

meeting the load requirements in a reliable

manner. A controller that monitors the operation

of the autonomous/grid-linked systems is

designed such a controller determines the energy

available from each of the system components

and the environmental credit of the system. It then

gives details related to cost, spilled energies, and

battery losses. Hybrid solar-wind applications are

implemented in the field, where all-year energy is

Figure 1: Hybrid System

to be consumed without any chance for an

interrupt. It is possible to have any combination

of energy resources to supply the energy demand

in the hybrid systems, such as oil, solar and wind.

This project is similar with solar power panel

and wind turbine power. Therefore, neither solar

Figure 2: Block diagram of proposed system

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nor wind power is sufficient alone. A number of

renewable energy expert claims to have a

satisfactory hybrid energy resource if both wind

and solar power are integrated within a unique

body. In the summer time, when sun beams are

strong enough, wind velocity is relatively small.

In the winter time, when sunny days are relatively

shorter, wind velocity is high on the contrast.

Efficiency of these renewable systems show also

differences through the year.

BLOCK DIAGRAM

A. Wind Power

Wind turbines are used to convert the wind power

into electric power. Electric generator inside the

turbine converts the mechanical power into the

electric power. Wind turbine systems are available

ranging from 50W to 2-3 MW. The energy

production by wind turbines depends on the wind

velocity acting on the turbine. Wind power is used

to feed both energy production and consumption

demand, and transmission lines in the rural areas.

Wind turbines can be classified with respect to

the physical features (dimensions, axes, number

of blade), generated power and so on. For

example, wind turbines with respect to axis

structure: horizontal rotor plane located turbines,

turbines with vertical or horizontal spinning

directions with respect to the wind. Turbines with

blade numbers: 3-blade, 2-blade and 1-blade

turbines .On the other hand, power production

capacity based .classification has four

subclasses.

• Small Power Systems

• Moderate Power Systems

• Big Power Systems & Megawatt Turbines

B. Solar Power

Solar panels are the medium to convert solar

power into the electrical power. Solar panels can

convert the energy directly or heat the water with

the induced energy. PV (Photo-voltaic) cells are

made up from semiconductor structures as in

the computer technologies. Sun beam is

absorbed with this material and electrons are

emitted from the atoms that they are bounded.

This release activates a current. Photovoltaic is

known as the process between beam absorbed

and the electricity induced. With a common

principle and individual components, solar power

is converted into the electric power. Solar

batteries are produced by waffling p-n

semiconductors. A current-volt characteristic of

the PV in the darkness is very similar to that of

diode. Under beam, electron flow and current

occurs. In closed-loop, PV current passes I-

through the external load. While in open-loop, the

current completes the circuit through the p-n

diode structure Solar batteries can berepresented with an equivalent circuit of a currentsource, a resistor and a diode in parallel, and anexternal load-resistor as seen in Figure.

It is possible to insert AC-DC converter,charger, accumulator, extra power source, andcontroller depending on the design differences inoperational and functional specifications. Solartype power system could be categorized into twotypes:

Line-independent systems: These are

established in absence of line electricity to provide

Figure 3: Equivalent circuit of solar battery

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electricity. Since the current in these systems are

DC and it must be also available overnight,

energy is stored in accumulators, DC-batteries.

In case of AC-Supply requirements for the

appliances, it is possible to use DC-AC inverter.

Line-Dependent systems: These systems do

not need DC Batteries, since the energy is served

to the demand with the help of an inverter. Line

electricity is being switched in use in case of

insufficient sun beam

LOAD: It is the component responsible to absorb

this energy and transform it into work. The

diversity, amount and complexity of the behavior

of the loads that could be connected to a

photovoltaic system make difficult to be modeled.

REGULATOR: It is the element to protect the

battery against to risking situations as overloads

and over discharges. The theoretical formulation

of the model can be simple, although it is

necessary to consider the peculiar discontinuities

of the model and the inter performance with the

rest of the analyzed models.

INVERTER: The inverter allows transforming the

DC current to AC. A photovoltaic installation that

incorporates an inverter can belong to two

different situations, based on the characteristics

of the alternating network .An isolated system,

where the inverter is the element of the network

and has to feed the set of loads. The inverter is

connected to the public network, to which it sends

the energy generated by the system. The model

must be able to include both situations.

CONVERTER: The positioning of a converter

between the panels and the batteries will improve

the whole photovoltaic installation, allowing

different controls from the system.

BOOST CONVERTER: As stated in the

introduction, the maximum power point tracking

is basically a load matching problem. In order to

change the input resistance of the panel to match

the load resistance (by varying the duty cycle), a

DC to DC converter is required. It has been

studied that the efficiency of the DC to DC

converter is maximum for a buck converter, then

for a buck-boost converter and minimum for a

Figure 4: Circuit Diagram of a Boost Converter

boost converter but as we intend to use our

system either for tying to a grid or for a water

pumping system which requires 230 V at the

output end, so we use a boost converter.

MODE 1 OPERATION OF THE BOOSTCONVERTER

When the switch is closed the inductor gets

charged through the battery and stores the

energy. In this mode inductor current rises

(exponentially) but for simplicity we assume that

the charging and the discharging of the inductor

are linear. The diode blocks the current flowing

Figure 5: Mode 1 Operation of Boost Converter

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and so the load current remains constant which

is being supplied due to the discharging of the

capacitor.

MODE 2 OPERATION OF THE BOOSTCONVERTER

In mode 2 the switch is open and so the diode

becomes short circuited. The energy stored in

the inductor gets discharged through opposite

through an isolation transformer and a coupling

inductor L, which may be provided partly or entirely

by the transformer leakage inductance. The full

bridge on the left hand side of Fig. 1 is connected

to the HV dc bus and the full bridge on the right

hand side is connected to the low-voltage (LV)ultra capacitor. Each bridge is controlled to

generate an HF square-wave voltage at its

terminals. By incorporating an appropriate value

of coupling inductance, the secondary two

square-waves can be suitably phase shifted with

respect to each other to control power flow from

one dc source to another. Thus, bidirectional

power flow is enabled through a small lightweight

HF transformer and inductor combination, and

power flows from the bridge generating the

leading square-wave.

Although various modes of operation of the

DAB converter have been presented recently for

high power operation, the square-wave mode is

supposedly the best operating mode. This is

because imposing quasi-square-wave on the

transformer primary and voltages results in

trapezoidal, triangular, and sinusoidal waveforms

of inductor current in the DAB converter ac link.

These modes are beneficial for extending the low-

power operating range of the converter although

these modes tend to reduce the switching losses;

the voltage loss is significant due to zero voltage

Figure 6: Mode 2 operation of Boost Converter

polarities which charge the capacitor. The load

current remains constant throughout the

operation.

C) Basic Principal of Operation of Bi-directional Converter

The DAB converter that has been validated under

certain operating conditions for low load, low

efficiency, and low-power operation, but the

device average and RMS current models and

transformer/inductor RMS current models which

could serve useful for hardware design was not

proposed. Moreover, such current models are not

available in the existing literature for either low-

power or high-power converter operation. A

comparative evaluation of single- and three-phase

versions of the DAB converter was performed

the perspective of operating performance and

losses for bidirectional power conversion

applications.

The DAB converter shown below in Fig. 1

consists of two full-bridge circuits connected

Figure 7: Schematic of the DAB dc-dc converter

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PARTICLE SWARM

OPTIMIZATION

The PSO algorithm works by simultaneously

maintaining several candidate solutions in the

search space. During each iteration of the

algorithm, each candidate solution is evaluated

by the objective function being optimized,

determining the fitness of that solution. The PSO

algorithm consists of three steps, which are

repeated until some stopping condition is met,

1. Evaluate the fitness of each particle

2. Update individual and global best fitness and

positions

3. Update velocity and position of each particle

After every iteration, each particle is updated

Table 1: Average Current Model of Devicesin Dab Converter for Boost mode (PowerTransfer from the LV Side to the HV Side)

Table 2: RMS Current Model of Devices inDab Converter for Boost Mode (Power

transfer from the LV side to the HV Side)

periods in the quasi-square-wave, which reduces

the effective power transfer at high-power levels.

Therefore, the contribution highlighted in this

paper forms important research on the DAB

converter.

Figure 8: Waveforms for A Boost Convert

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by following two “best” values. The first one is

the best solution (fitness) it has achieved so far.

The fitness value is also stored. This value is

called “pbest”. Another “best” value that is tracked

by the particle swarm optimizer is the best value

obtained so far by any particle in the population.

After every iteration, each particle is updated

by following two “best” values. The first one is

the best solution (fitness) it has achieved so far.

The fitness value is also stored. This value is

called “pbest”. Another “best” value that is tracked

by the particle swarm optimizer is the best value

obtained so far by any particle in the population.

To understand the algorithm, it is best to

imagine a swarm of birds that are searching for

food in a defined area - there is only one piece of

food in this area. Initially, the birds don’t know

where the food is, but they know at each time

how far the food is. Which strategy will the birds

follow? Well, each bird will follow the one that is

nearest to the food. PSO adapts this behavior

and searches for the best solution-vector in the

search space. A single solution is called particle.

Each particle has a fitness/cost value that is

evaluated by the function to be minimized, and

each particle has a velocity that directs the “flying”

of the particles. The particles fly through the

search space by following the optimum particles.

The algorithm is initialized with particles at

random positions, and then it explores the search

space to find better solutions. In every iteration,

each particle adjusts its velocity to follow two best

solutions. The first is the cognitive part, where

the particle follows its own best solution found so

far. This is the solution that produces the lowest

cost (has the highest fitness). This value is called

pBest (particle best). The other best value is the

current best solution of the swarm, i.e., the best

solution by any particle in the swarm. This value

is called gBest (global best). Then, each particle

adjusts its velocity and position with the following

equation:

v’ = v + c1.r1.(pBest - x) + c2.r2.(gBest - x)

x’ = x + v’

v is the current velocity, v’ the new velocity, x

the current position, x’ the new position, pBest

and gBest as stated above, r1 and r2 are even

distributed random numbers in the interval [0, 1],

and c1 and c2 are acceleration coefficients.

Where c1 is the factor that influences the

cognitive behavior, i.e., how much the particle will

follow its own best solution and c2 is the factor

for social behavior, i.e., how much the particle

will follow the swarm’s best solution.

1. The algorithm can be written as follows:

Initialize each particle with a random velocity

and random position.

2. Calculate the cost for each particle. If the

current cost is lower than the best value so

far, remember this position (pBest).

3. Choose the particle with the lowest cost of all

particles. The position of this particle is gBest.

4. Calculate, for each particle, the new velocity

and position according to the above equations.

Figure 9: THD Analysis using PSO

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Repeat steps 2-4 until maximum iteration or

minimum error criteria is not attained.

EXPECTED RESULT

SIMULATIONS AND RESULTS

The proposed system is implemented in

simulation software platform. The details of

software required, coding and the experimental

results are further discussed here. The work is

done in MATLAB Version 7.12.0(R2011a). The

Image processing toolbox is made use of for the

work. The work is executed in Graphic User

Figure 10: Simulation diagramand Simulation results

WIND OUTPUT VOLTAGE

PV OUTPUT VOLTAGE

DC COMMON GRID OUTPUT VOLTAGE

AC GRID THREE PHASE OUTPUT VOLTAGE

AC PHASE TO LINE VOLTAGE

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FILTERES OUTPUT VOLTAGE

OUTPUT HARMONICS

Interface (GUI). The high dynamic range image

is produced using the software Adobe Photoshop

CS. MATLAB is a high-performance language for

technical computing. It integrates computation,

visualization, and programming in an easy-to-use

environment where problems and solutions are

expressed in familiar mathematical notation.

CONCLUSION AND FUTURE

WORK

The suggested methodology has considered both

accuracy of obtained solutions and computational

overhead of the hybrid system. This paper has

described techniques for particle swarm

optimization. In this proposed method, a hybrid

power generation system including wind power,

solar power and storage batteries is designed on

the basis of cost and their simulation results are

discussed for better understanding. The hybrid

offers better reliability and lower cost compared

to individual renewable sources of energy. The

use of renewable energy offers substantial

environmental credit when compared to

conventional alternatives. The proposed analysis

allows the user to study the interaction among

economic and operational factors and hence it

offers a useful tool for the design and analysis of

cost effective hybrid power systems. This work

can be further carried out in the future by including

the instability in the availability of the renewable

energy sources over a period of time.

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