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
1. Introduction

Reason choosing this research topic

Reduction harmonic method

Proposed
controller:
Inference System)
ANFIS
(Adaptive
Neuro
Fuzzy
2. Shunt Active Filter Modeling


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
is
iL
Supply
iL
is
iF
iF
Non-linear
Shunt Active
Filter
Fig. 1. Power system with non-linear load and shunt active filter.
2. Shunt Active Filter Modeling
is
iL
Supply
iL
is
iF
iF
Non-linear
Shunt Active
Filter
iF  iL  iS
(1)
iL  i f  iH
(2)
iF  iH
(3)
Fig. 1. Power system with non-linear load and shunt active filter.
From (1), (2) we have:
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.
2. Shunt Active Filter Modeling
The mathematical model can be extracted from the single-phase equivalent
scheme by Fig. 2.
R
L
R
L
S
C
C
iS vS
S
iF
Lf
eS
vF
iL
AF
Fig. 2. Single-phase equivalent scheme
diF
LF
 vF  vS
dt
(4)
vS  eS  RS iS  LS
diS
(5)
dt
vF   E
(6)
iFa   1/ LF
d   
iFb 
0
dt   
iFc   0
iSa    RS / LS
d   
iSb 
0
dt   
iSc   0
0
1/ LF
0
0
 RS / LS
0
 vFa  vSa 
0  . vFb  vSb 
1/ LF   vFc  vSc 
0
 iSa 
1
0  . iSb  
LS
 RS / LS  iSc 
0
(7)
vSa  eSa 
v  e 
 Sb Sb  (8)
 vSc  eSc 
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 iS vS
RC
iL
LC
Non-liner
load
Lf
eS
BPF
vF
AF
iF
PWM
LPF
iref
Controller
Inverter
Fig. 3. The active filter control structure.
Where: AF is active filter; BPF is band pass filter; LPF is low pass filter; PWM
is pulse width modulation.
3. Control System Design
3.1 Control structure of Active Filter
Vabc
A
uA
Rs
Ls
Us
Iabc
Is
B
a
a
A
b
b
B
B
c
c
C
C
A
uB
Rc
Lc
Discrete,
Ts = 5e-006 s.
C
uC
powergui
Three-Phase
V-I Measurement
I_load Measurement
Load
ilA
a
A
Out1
b
B
Out2
In1
Fo=50Hz
Fo=50Hz
Lf
c
C
G
Out3
G
In2
ilB
Fo=50Hz
Fo=50Hz
Fo=50Hz
Fo=50Hz
In3
ilC
I_Filter Measurement
Active Filter
Controller
I_load
ifA
ifB
ifC
I_Filter
Fig. 4. The simulation model of electrical power system with active filter.
3. Control System Design
3.1 Control structure of Active Filter
1 Out1
1
e1
2 Out2
3 Out3
K
IGBT3
2
e2
g
C
IGBT5
E
IGBT1
E
boolean
C
g
LPF1
C
Relay a1
Controller 1
E
Derivative
g
t.s+1
du/dt
K
t.s+1
du/dt
Derivative1
Relay a2
Controller 2
3
e3
LPF2
1
G
1
boolean
E
G
boolean
C
g
C
g
C
R
IGBT2
E
IGBT6
E
IGBT4
E
Relay a3
LPF3
C
t.s+1
du/dt
Derivative3
g
K
Controller 3
Carrier wave
Fig. 5. The controller structure.
Fig. 6. Active Filter structure using IGBTs
3. Control System Design
3.2 Fuzzy Logic Controller for AF
iref
vS
e
u
de
dt
de
K
AF
tS+1
iF
Fuzzy Logic
Controller
Fig. 7. Fuzzy controller synoptic diagram
150
100
u
50
0
-50
-100
-150
200
0
-200
de
Fig. 8 Rule viewer window.
-30
-20
0
-10
10
20
30
e
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.
3. Control System Design
3.3 ANFIS Architecture for AF
A1
x1
x1
A2
P
B1
P
w1
N
y1
w1
w1f1
å
y1
N
w2
w2f2
w2
x1
B2
Layer 1
Layer 2
Layer 3
y1
Layer 4
Fig. 10. ANFIS architecture
Layer 1 consists of input variables
Layer 2 is membership layer
Layer 3 is rule layer
Layer 4 is defuzzification layer
Layer 5 is output layer
Layer 5
f
3. Control System Design
3.3 ANFIS Architecture for AF
Fig. 11. 500 patterns are loaded into the ANFIS editor tool
Fig. 12. Result of the ANFIS model testing with training data
3. Control System Design
Degree of membership
3.3 ANFIS Architecture for AF
1 N
ZE
P
0.8
0.6
0.4
0.2
0
-30
-20
-10
0
e
10
20
30
Degree of membership
Fig. 13. Membership functions of input e.
1
N
P
ZE
0.8
0.6
0.4
0.2
0
0
20
40
60
80 100 120 140 160 180 200
e
Fig. 14 Tuned membership functions of input e
3. Control System Design
Degree of membership
3.3 ANFIS Architecture for AF
1N
P
0.8
0.6
0.4
0.2
0
-300
-200
-100
0
100
200
300
de
Degree of membership
Fig. 15. Membership functions of input de.
P N
1
0.8
0.6
0.4
0.2
0
-4
-3
-2
-1
0
1
2
3 x 105
de
Fig. 16 Tuned membership functions of input de
4. Simulation Results
Table 1. Simulation parameters
4. Simulation Results
Magnitude [A]
400
200
0
-200
-400
0
0.02
0.04
0.06
Time (s)
0.08
0.1
0.12
Fig. 17. Supply current isa waveform before applying the AF.
THD= 12.54%
Mag (% of Fundamental)
100
80
60
40
20
0
0
2
4
6
8
10
Harmonic order
12
14
16
18
Fig. 18. Harmonic spectrum of isa before applying AF
20
4. Simulation Results
200
0
-200
-400
0
0.02
0.04
0.06
Time (s)
0.08
0.1
0.12
Fig. 19. Supply current isa waveform after applying AF using FLC
THD= 1.04%
100
Mag (% of Fundamental)
Magnitude [A]
400
80
60
40
20
0
0
2
4
6
8
10
12
Harmonic order
14
16
18
20
Fig. 20. Harmonic spectrum of isa after applying AF using FLC
4. Simulation Results
Magnitude [A]
400
200
0
-200
-400
0
0.02
0.04
0.06
Time (s)
0.08
0.1
0.12
Fig. 21. Supply current isa waveform after applying AF using ANFIS
THD= 0.98%
Mag (% of Fundamental)
100
80
60
40
20
0
0
2
4
6
8
10
12
Harmonic order
14
16
18
20
Fig. 22. Harmonic spectrum of isa after applying AF using ANFIS
4. Simulation Results
500
400
Magnitude [A]
300
200
100
0
-100
iref
iFa using ANFIS
-200
0
0.02
0.04
0.06
0.08
0.1
Time (sec)
Fig. 23. AF current and its reference with ANFIS.
0.12
4. Simulation Results
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.