International Journal of Soft Computing, Mathematics and Control (IJSCMC),Vol.2,No.2,May 2013
REDUCTION IN THE TRANSIENT TIME OF
SHUNT ACTIVE FILTERS USING INTELLIGENT
CONTROLLERS
Sakshi Bangia1, Maneesha Garg2 and P.R.Sharma3
1,2
Department of Electrical Engineering, YMCA University of Science and Technology,
Faridabad, Haryana
sakshibangia@gmail.com1,prsharma1966@gmail.com2
3
Department of Humanities of Applied Science, YMCA University of Science and
Technology
garg_maneesha@yahoo.com3
ABSTRACT
This paper deals with the implementation of new proposed strategy for the reduction in the transient time of
shunt active filter using intelligent controllers. Intelligent control is a class of control techniques that use
various AI computing approaches like fuzzy logic, neural network, evolutionary computation, genetic
algorithm etc. The proposed strategy works on the principle of energy of the capacitor to maintain the DClink voltage of shunt connected active filter and thus reduces the transient time whenever there is sudden
change in the load. A comparison using the PI controller, Fuzzy logic controller and Artificial neural
controller has been made using MATLAB Simulink Power System Toolbox.
KEYWORDS
Power Quality, Active Power Filter, Transient Time, Fuzzy Logic, Artificial Neural Network
1.INTRODUCTION
Power Quality is defined as the extent to which both the utilization and distribution distresses the
electric power system affects the efficacy of electrical equipment. Conventionally Passive filters
were used to reduce the Total Harmonic Deduction (THD) and compensate the reactive power.
Passive filters were considered to most reliable, cost effective, robust, and can be easily
maintained. But they suffer from certain disadvantages like create resonance with the system,
they are bulky and the most prominent is that they are tuned for particular harmonic frequency. A
modern alternative to the passive filter is application of the Shunt Active Filters (SAF) which uses
closed feedback loops, gives enhanced dynamics and control of harmonic .Basically SAF
represents a controlled current source which is added to the load current to give sinusoidal source
current. SAF provides harmonic compensation and reactive power compensation much more
effectively than passive filters. APF consist of Voltage-source inverters and a DC capacitor; have
the ability to adjust the amplitude of the synthesized ac voltage of the inverters by means of pulse
width modulation or by control of the DC-link voltage, thus drawing either leading or lagging
reactive power from the supply. [1]-[5]. Synchronous Reference Frame (SRF) control strategy is
employed for the control of SAF [6]. The present research work emphasis on the comparison of
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International Journal of Soft Computing, Mathematics and Control (IJSCMC),Vol.2,No.2,May 2013
the transient time of DC voltage using proposed energy based control strategy based on the
different intelligent controllers.
2. PRINCIPLE OF OPERATION
Fig 1 Block diagram of shunt connected active filter
Fig 1 shows the block diagram of shunt connected active filter.The shunt active filter consists of a
VSI with capacitor on the DC side. The shunt active filter injects a harmonic currents with the
same amplitude of those of the load into the ac system but with opposite phase displacement.
Synchronous reference frame method is utilized to extract the harmonic content of the load and
thus allows controlling the active filters in real-time system [7].
Fig 2 Synchronous Reference Frame Control Strategy
The load currents (iLa, iLb, iLc), the PCC voltages (vsa, vsb, vsc) and DC bus voltage (vdc) of SAF are
sensed as feedback signals. The load currents (iLa, iLb, iLc) from the a-b-c frame are converted to
the d-q-o frame. The DC voltage loop is responsible for keeping constant DC voltage. The output
of this PI controller is id*, input to which is the deviation of Vdc from Vdc*.The current id* is
responsible for harmonic mitigation. The AC voltage control loop is activated for voltage
regulation and reactive power compensation. The current iq* is output of PI controller, the input
to which is the deviation of the PCC voltage Vpcc as compared to a reference.
Conventionally the DC voltage control loop is given by
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International Journal of Soft Computing, Mathematics and Control (IJSCMC),Vol.2,No.2,May 2013
p
Where
k
pdc
and
k
idc
dc
= k pdc e + k idc ∫ edt
(1)
are the proportional and integral gains respectively for the DC
voltage control loop and e is the error between the reference DC voltage and the average DC
voltage.
The Hysteresis current controllers are accomplished in the three phases independently [8]. Each
current controller determines the switching signals to the inverter. The error signal reference and
actual source current are calculated and compared within a small hysteresis band hb. The
switching logic for phase a is formulated as below;
If ifa < ( i*fa -hb) upper switch of VSC turned OFF and lower switch is ON
If i fa < ( i*fa +hb) upper switch of VSC is turned ON and lower switch is OFF
In the same fashion, the switching of phase b and c devices are derived.
3. PROPOSED ENERGY BASED CONTROL STRATEGY
In this paper a new control strategy based on energy is implemented using fuzzy logic control or
ANN in place PI control. The energy demanded by the capacitor to charge from actual
voltage to the reference value can be computed as[9]
w
dc
=
1
2
2
(
− vdc )
c
dc vdcref
2
(2)
Now the total DC power required by the capacitor is computed using PI controller is as follows:
2
2
2
2
p = k (v − v ) + k ∫ (v − v ) (3)
dc
pe
dcref
dc
ie
dcref
dc
3.1. Proposed Energy based FUZZY Logic controller
Artificial Intelligence techniques are the recent trends used for the enhancement of power quality
.The fuzzy-logic based controllers are also used for the control circuit design of active filters. A
fuzzy control system is a real-time expert system, implementing a part of a human operator’s or
process engineer’s expertise which does not lend itself to being easily expressed in PIDparameters or differential equations but rather in situation/action rules. A Fuzzy controller
composed of stages viz fuzzification, knowledge base, and inference mechanism. The knowledge
base is composed of data base and a rule base and is designed to obtain good dynamic response
under ambiguity in process parameters. The data base consists of input and output membership
functions. The inference mechanism uses a ensemble of linguistic rules to covert the input
condition in to a fuzzified output. Finally defuzzification is used to convert the fuzzy output in to
control signals.
In the proposed energy based control strategy, input to fuzzy logic controller is the error between
the squared DC reference voltage and square of the sensed PCC voltage.
In this case, three fuzzy subsets viz SN (Negative Small), (Zero), and SP (Positive Small) have
been chosen. Membership functions used for the input and output variables used here are shown
in Fig.4. As both inputs have three subsets, a fuzzy rule base formulated for the present
application is given in Table 1.
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International Journal of Soft Computing, Mathematics and Control (IJSCMC),Vol.2,No.2,May 2013
Fig 4 membership functions for input and output variables
change of
error
SN
S
SP
error
SN
PS
Z
SN
S
Z
PS
Z
SP
SN
Z
PS
Table 1 Fuzzy Rules
3.2. Proposed Energy based Artificial Neural Network controller
Neural network is a black–box method approach which works on the principle of biological
nervous system. The neural network processes the information in a similar way as human brain
does. They consider the behaviour of the brain as the network of units called neurons. [10].
Conventionally PI controllers are used to maintain the DC capacitor voltage constant .The
presented research work implements energy based control strategy, input to ANN controller is the
error between the squared DC reference voltage and square of the sensed PCC voltage.
To reduce the transient time when load changes abruptly, a multilayer feedforward- type ANNbased controller is designed. This network is designed with three layers, the input layer with 1,
the hidden layer with 30, and the output layer with 1 neuron, respectively. The neural network is
trained by specifying the input data (square of the difference between the Vdc reference voltage
and Vdc voltage) and target data (representing active power loss) ,’tansig’ transfer functions
between layers, 'trainlm’ and 1000 the number of epochs.
4. SYSTEM CONFIGURATION AND SIMULATION RESULTS
The three phase source, three phase unbalanced nonlinear resistive-inductive load, active filter
connected in shunt are modelled in MATLAB using Power System Blockset. The setup used to
estimate the performance of the SAF with proposed control scheme through simulation. The
source block comprises of a three-phase voltage source “infinite bus” with series impedance
signifying the relevant short-circuit impedance of the supplying grid to estimate the performance
of the SAF with proposed scheme under specified controllers. The considered loads to evaluate
the effectiveness of the proposed scheme are diode rectifier with inductive filter on source side
and star connected unbalanced resistive-inductive (RL) load connected to ac mains .The rectifier
fed load has been modelled as diode rectifier with inductive and resistive load. The performance
of active filter is demonstrated to minimize the transient time. The model is analysed under
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International Journal of Soft Computing, Mathematics and Control (IJSCMC),Vol.2,No.2,May 2013
switching of load from full load to half load at 0.4s to 0.8s.The system parameters are given in
Table 2
Whenever there is sudden reduction in the load, it causes an increase in capacitor voltage above
the reference value. Hence the capacitor absorbs surplus power from the source. Based on the
controller action, the capacitor voltage controller will be brought back to the constant value after
a few cycles. Similarly, when the load is switched back to the full load at instant 0.8 s, the DC
capacitor supplies power to the load momentarily and, hence, the DC voltage falls below the
reference value. The DC capacitor supplies power to the load momentarily. Again due to the
controller action, the capacitor voltage will gradually build up and reach to a steady state value.
Thus the time taken by the capacitor voltage to reach a steady value is termed as transient time.
System Parameters
Supply Voltage
Unbalanced Load
Non-Linear Load
Reference DC
voltage
DC capacitor
Interfacing
inductance
Hysteresis Band
Values
415 V(L-L),50 Hz
Za=25Ω
Zb= 44 Ω and
81mH
Zc=44 Ω and 81mH
Three phase full
wave Rectifier
drawing 5A current
550 V
2200µF
5mH
± 0.2A
Table2. Parameters of Considered System
The transient performance of active filter is implemented using conventional method and the
proposed energy based controller. Comparison of PI controller, fuzzy controller and ANN
controller are made in implementing conventional and energy based control strategy.
4.1. Transient performance by PI controller method
The transient performance of active filter during non-linear load unbalanced load condition is
simulated by making sudden changes in the ac load. In the simulation study, the load is halved at
the instant t= 0.4 s and brought back to full load at t=0.8s.
Fig 5 shows the comparison between transient time taken by the DC voltage using conventional
control method by PI controller and the proposed Energy based PI controller method. By
conventional method based PI controller ,when switching of load takes place transients starts
from 0.8s at the instant when load gets half to full load and it gets stable at 0.815s and after
0.815s DC voltage follows the repeated pattern to get stable. Similar observations can be found at
the instant when at t=0.4 full load gets change to half load.
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International Journal of Soft Computing, Mathematics and Control (IJSCMC),Vol.2,No.2,May 2013
Fig 5 Comparison between transient time by using conventional method by PI controller and the Energy
based PI controller method
From fig 5 it can be clearly analysed that the time taken by the transient using energy based
method 0.8s to 0.811sAfter 0.811s the DC voltage repeats itself and get stable.
4.2. Transient performance by Fuzzy Logic controller method
Fuzzy provides a remarkably simple way to draw definite conclusions from vague, ambiguous or
imprecise information. Fig 6 shows the comparison between transient time taken by the DC
voltage using conventional control method by FUZZY Logic controller and the proposed Energy
based FUZZY Logic controller method. By conventional method based Fuzzy controller ,when
switching of load takes place transients starts from 0.4s at the instant when load gets to half load
and it gets stable at 0.418s . After 0.418s DC voltage follows the repeated pattern to get stable.
Similar observations can be found at the instant when at t=0.8 halved load gets change to full
load.
Fig 6 Comparison between transient time by using conventional method by Fuzzy Logic controller and the
Energy based Fuzzy Logic controller method
From fig 6 it can be clearly analysed that the time taken by the transient using energy based
method 0.4s to 0.407s. After 0.0.407s the DC voltage repeats itself and get stable.
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International Journal of Soft Computing, Mathematics and Control (IJSCMC),Vol.2,No.2,May 2013
4.3. Transient performance by ANN controller method
ANN architecture can be ‘trained’ with known example of a problem before they are tested for
their inference capability on unknown instances of the problem. Fig 7 shows the comparison
between transient time taken by the DC voltage using conventional control method by ANN
controller and the proposed Energy based ANN controller method. By conventional method
based ANN controller ,when switching of load takes place transients starts from 0.4s at the instant
when load gets to half load and it gets stable at 0.413s . After 0.413s DC voltage follows the
repeated pattern to get stable. Similar observations can be found at the instant when at t=0.8
halved load gets change to full load.
Fig 7 Comparison between transient time by using conventional method by ANN controller and the Energy
based ANN controller method
From fig 7 it can be clearly analysed that the time taken by the transient using energy based
method 0.4s to 0.406s. After 0.0.406s the DC voltage repeats itself and get stable
The comparison of the transient time of dc voltage when there is sudden decrease in load by
various methods has been formulated in Table 3. It can be clearly analysed that Energy Based
method with fuzzy controller and ANN Controller gave comparable results prove to be the
preeminent among all.
Controller
PI
method
Conventional 0.015 s
method
Energy
0.011 s
Based
method
FUZZY
ANN
CONTROLLER CONTROLLER
0.018 s
0.013s
0.007 s
0.006s
Table 3 Comparison of Transient Time
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International Journal of Soft Computing, Mathematics and Control (IJSCMC),Vol.2,No.2,May 2013
5. CONCLUSIONS
A new concept of Energy based controller has been introduced. This Paper has proposed the
development of Shunt Active Filter for the reduction of transient time and significant comparison
has been made using PI, Fuzzy Logic and ANN controller. Simulation results with MATLAB
show that Energy based ANN controller has very quick response time, making a better control
strategy for shunt active filter.
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Authors
Sakshi Bangia received the B.Tech degree in Instrumentation and Control from MDU
Rohtak and M.Tech degree in Electrical Engg. from YMCA UST in 2004 and 2006,
respectively. . She is pursuing Ph.D in Power Quality from Maharishi Dayanand University,
India. Presently she is working as Assistant Professor in Electrical Engineering Department
at YMCA UST Faridabad.
Dr.Maneesha Garg did her Ph.D(Physics) in 2002 from Kurukshetra University, India.
After that she worked as Research Associate granted by CSIR, in NIT Kurukshetra for 4
years. She has 18 publications in national, international journals and more than 30 papers in
conferences to her credit. Presently she is working as Assistant Professor in Humanities and
Applied Science Department, YMCA UST, Faridabad and guiding 4 scholars for their
research work.
Dr. P.R.Sharma was born in 1966 in India. He is currently working as Head of the
Department in Electrical Engg. in YMCA University of Science and Technology,
Faridabad. He received his B.E Electrical Engineering in 1988 from Punjab University
Chandigarh, M.Tech in Electrical Engineering (Power System) from Regional Engineering
College Kurukshetra in 1990 and Ph.D from M.D.University Rohtak in 2005. He started his
carrier from industry. He has vast experience in the industry and teaching. His area of
interest is Optimal location and coordinated control of FACTS devices ,power system stability and control.
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