application of anfis controlled shunt active filter for harmonic reduction

Application of ANFIS Controlled Shunt Active Filter for

Harmonic Reduction

Authors : Chun-Tang Chao, Chi-Jo Wang, Cheng-Ting Hsu, Nguyen Thi Hoai Nam

Presented by : Nguyen Thi Hoai Nam

OUTLINE

1. Introduction

2. Shunt Active Filter Modeling

3. Control System Design

4. Simulation Results

5. Conclusions

Reason choosing this research topic

Reduction harmonic method

Proposed controller: ANFIS (Adaptive Neuro Fuzzy

Inference System)

1. Introduction


Active filter is a power electronic device based on the use of inverters

Shunt Active Power Filter is connected in a common point connection between the source of power system and the load system which present the source of the polluting currents circulating in the power system lines

Non-linear is

Supply is iL

iF iF

Shunt Active Filter

iL

Fig. 1. Power system with non-linear load and shunt active filter.


Non-linear is

Supply is iL

iF iF

Shunt Active Filter

iL

F Hi i

Formula (3) indicates that purpose of shunt active power filter is intended to generate exactly the same harmonics contained in the polluting current iL but with opposite phase.

L f Hi i i

F L Si i i (1)

(2)

From (1), (2) we have: (3)

Fig. 1. Power system with non-linear load and shunt active filter.


The mathematical model can be extracted from the single-phase equivalent scheme by Fig. 2.

AF

RS LS iS

vF

vS

iL

iF

eS

RC LC

Lf

FF F S

diL v vdt

SS S S S S

div e R i L

dt

Fv E

(4)

(5)

(6)

1/ 0 00 1/ 0 .0 0 1/

Fa F Fa Sa

Fb F Fb Sb

Fc F Fc Sc

i L v vd i L v vdt

i L v v

/ 0 010 / 0 .

0 0 /

Sa S S Sa Sa Sa

Sb S S Sb Sb SbS

Sc S S Sc Sc Sc

i R L i v ed i R L i v edt L

i R L i v e

(7)

(8)

Fig. 2. Single-phase equivalent scheme

3. Control System Design 3.1 Control structure of Active Filter

Fig. 3 is applied to control AF producing current track with the load current harmonic

RS LS iL RC LC

eS

iS vS

PWM Controller

Non-linerload

BPF

AF

vF

iref

Lf

Inverter

iF

LPF

Where: AF is active filter; BPF is band pass filter; LPF is low pass filter; PWM is pulse width modulation.

Fig. 3. The active filter control structure.


uC

uB

uA

Discrete,Ts = 5e-006 s.

powergui

Us

Vabc

Iabc

A

B

C

a

b

c

Three-PhaseV-I Measurement

Rs Ls

A

B

C

Load

Lf

Is

a

b

c

A

B

C

I_load Measurement

ilA

ilB

ilC

I_load

a

b

c

A

B

C

I_Filter Measurement

ifA

ifB

ifC

I_Filter

In1

In2

In3

G

Controller

G

Out1

Out2

Out3

Active Filter Fo=50Hz

Fo=50Hz

Fo=50Hz

Fo=50Hz

Fo=50Hz Fo=50Hz

Rc Lc

Fig. 4. The simulation model of electrical power system with active filter.


E

3 Out32 Out21 Out1

Rg CE

IGBT6

g CE

IGBT5

g CE

IGBT4

g CE

IGBT3

g CE

IGBT2

g CE

IGBT1

boolean

boolean

boolean

C

1

G

Fig. 6. Active Filter structure using IGBTs

Controller 1

Controller 2

Controller 3

1G

K

t.s+1

LPF3

K

t.s+1

LPF2

K

t.s+1

LPF1

Relay a3

Relay a2

Relay a1

du/dt

Derivative3

du/dt

Derivative1

du/dt

Derivative

Carrier wave

3e3

2e2

1e1

Fig. 5. The controller structure.

3. Control System Design 3.2 Fuzzy Logic Controller for AF

iref

dedt

e

ude

Fuzzy Logic Controller

KtS+1

AFiF

vS

Fig. 7. Fuzzy controller synoptic diagram

-30-20

-100

1020

30

-200

0

200

-150

-100

-50

0

50

100

150

edeu

Fig. 8 Rule viewer window. Fig. 9 Relationship between e, de, u

3. Control System Design 3.3 ANFIS Architecture for AF

Jang originally presented the Adaptive Neuro-Fuzzy Inference System technique in 1993 [16]. Jang combined both Fuzzy Logic and Neural Network to produce a powerful processing tool named Neuro-Fuzzy Systems that have both Neural Network and Fuzzy Logic advantages and the most common one is ANFIS. Actually, this tool is like a fuzzy inference system, but the difference is in the use of a back propagation algorithm for minimizing the error.


A1

A2

x1

B1

B2

y1

P N

Nå

P

x1

x1

y1

y1

f

w1

w2

w1

w2

w1f1

w2f2

Layer 1 Layer 2 Layer 3 Layer 4 Layer 5

Layer 1 consists of input variables Layer 2 is membership layerLayer 3 is rule layerLayer 4 is defuzzification layer Layer 5 is output layer

Fig. 10. ANFIS architecture


Fig. 11. 500 patterns are loaded into the ANFIS editor tool

Fig. 12. Result of the ANFIS model testing with training data


-30 -20 -10 0 10 20 30

0

0.2

0.4

0.6

0.8

1

e

Degr

ee o

f mem

bers

hip

N ZE P

Fig. 13. Membership functions of input e.

Fig. 14 Tuned membership functions of input e

0 20 40 60 80 100 120 140 160 180 200

0

0.2

0.4

0.6

0.8

1

e

Deg

ree

of m

embe

rshi

p

N ZE P


Fig. 15. Membership functions of input de.

Fig. 16 Tuned membership functions of input de

-300 -200 -100 0 100 200 300

0

0.2

0.4

0.6

0.8

1

de

Degr

ee o

f mem

bers

hip

N P

-4 -3 -2 -1 0 1 2 3 x 105

0

0.2

0.4

0.6

0.8

1

de

Degr

ee o

f mem

bers

hip

NP


Table 1. Simulation parameters


0 0.02 0.04 0.06 0.08 0.1 0.12

-400

-200

0

200

400

Time (s)

Mag

nitu

de [A

]

Fig. 17. Supply current isa waveform before applying the AF.

0 2 4 6 8 10 12 14 16 18 200

20

40

60

80

100

Harmonic order

THD= 12.54%

Mag

(% o

f Fun

dam

enta

l)

Fig. 18. Harmonic spectrum of isa before applying AF


Fig. 19. Supply current isa waveform after applying AF using FLC

Fig. 20. Harmonic spectrum of isa after applying AF using FLC

0 0.02 0.04 0.06 0.08 0.1 0.12

-400

-200

0

200

400

Time (s)

Mag

nitu

de [A

]

0 2 4 6 8 10 12 14 16 18 200

20

40

60

80

100

Harmonic order

THD= 1.04%

Mag

(% o

f Fun

dam

enta

l)


Fig. 21. Supply current isa waveform after applying AF using ANFIS

Fig. 22. Harmonic spectrum of isa after applying AF using ANFIS

0 0.02 0.04 0.06 0.08 0.1 0.12

-400

-200

0

200

400

Time (s)

Mag

nitu

de [A

]

0 2 4 6 8 10 12 14 16 18 200

20

40

60

80

100

Harmonic order

THD= 0.98%

Mag

(% o

f Fun

dam

enta

l)


0 0.02 0.04 0.06 0.08 0.1 0.12-200

-100

0

100

200

300

400

500

Time (sec)

Mag

nitu

de [A

]

irefiFa using ANFIS

Fig. 23. AF current and its reference with ANFIS.


Table 2. Total Harmonic Distortion (THD) (%) in different running conditions of load

5. Conclusions

In this work, the FLC and ANFIS are developed to reduce the harmonic current for nonlinear loads through running simulation in Matlab/Simulink environment.

Importantly, the applied ANFIS controller is better than the fuzzy controller and can also be used to improve the control performance of nonlinear systems.

Experimental results and simulations show that the resulting shunt active filter presents good dynamic and steady-state response. Harmonic pollution is always kept under IEEE 519 standards.

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application of anfis controlled shunt active filter for harmonic reduction

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