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Fuzzy Logic Controlled Three Phase Active Power
Filter for mitigation of harmonic and volt ampere
compensator
H.R.Imanijajarmi, AzahMohamad
Department of Electrical, Electronic & Systems Engineering
National University of Malaysia, Malaysia
ABSTRACT
Harmonic pollution is a serious and a damaging problem in electric power systems.
Active power filtering represents one of the most effective proposed solutions.The active power filter is operated to compensate harmonics and reactive power
generated by the non-linear load and power factor correction simultaneously.A fuzzy logic based control is developed to regulate the voltage of the DC capacitor.The system with control scheme is implemented in Matlab/Simulink.The simulation
results showquite satisfactory to mitigate harmonic distortions, reactive power
compensation and power factor improvement.
Keywords -Active Power Filter, Fuzzy logic controller,reference source current,nonlinear Load.
.1 INTRODUCTION
Nowadays, the applications of power electronics have grown rapidly. These powerelectronic systems provide highly nonlinear characteristics. These loads draw non-
sinusoidal currents that cause harmonic voltage drops across the network impedance,resulting in distorted voltages ]1[ . The shunt active powerfilter (APF) shows a mighty
strength in eliminatingreactive power andharmonics which has receivedwide concernin power electronic filed [2,3].
The shunt active power filters (APF), generally based on a voltage source inverter
structure, and seems to be an attractive solution to harmonic current pollution problems.In agreement with PWM control laws,By keeping the DC-link voltage of
inverter constant ,APF can compensate harmonics and reactive power effectively , fordo thissome actions must be taken because APF itself cannot produce powertomaintain the DC-link voltage[4]. Mostlyconventional PI controller by adding an
active component to the source current reference isused to control DC-link voltage[5,6]. Nevertheless,The PI controller based approach requires precise linearmathematical model which is difficult to obtain. Also, it fails to perform satisfactorilyunder parameter variations, non-linearity, and load disturbances, etc.
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As a result ,when APF is plunged into the system ,It willcreate, DCvoltage overshootand inrush source current willlead to protection or even equipment damage. The
voltage overshoot andinrush current have been the constriction which limitsthedevelopment of APF.Recently, fuzzy logic controllers (FLCs) have generated a good deal of interest in
certain applications [7]-[9]. The main advantages of fuzzy controlover conventionalcontrollers are its linguistic description, independence of mathematical model,
robustness, and its universal approximation [10]. This paper proposes a fuzzy logic
controller for D.C bus voltage control [11]. Simulations results present theeffectiveness of the proposed approach in suppressing the harmonics andreactive
power.
.2 Shunt Active Power Filter
.2.1 Basic compensation principals
Fig 1 shows a current controlled voltage source inverter with necessary passivecomponents is used as an APF.It is controlled to draw/supply a compensated current
iffrom/to the utility, so that it cancels current harmonics on the ac side and makes the
source current in phase with the source voltage. In other words, removes reactive andharmonic currents of the non-linear load. Thus, the resulting total current drawn from
the ac mains is sinusoidal. Ideally, the APF requires to generate just the enoughreactive and harmonic current to satisfy the non-linear loads in the line. [12]-[14].
Fig.1.Connection of shunt active filter with non-linear load
.2.2 reference source currents
Source voltage is given by
vSt=Vm sin t (1)the instantaneous currents can be written as:iSt=iLt-iC(t) (2)
If a nonlinear load is applied, then the load current will have a fundamental
component and harmonic components, which can be represented as
iLt= In sin (nt+n=1 n) (3)=I1 sint+1+ In sin (nt+n=2 n)
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The instantaneous load power canbe given as
pLt=vS(t)*iL(t) (4)
=VmI1sin2t *cos 1+VmI1 sint* cos t*sin 1+
Vm sin t* sin (nt+n)
n=2
=pf(t)+p
r(t)+p(t)
From (4), the real (fundamental) power drawn by the load is
pf(t) =vS(t)*iS(t) (5)
From (5), the sourcc current supplied by the source, after compensation.is
iS )t( =pf(t)/vS(t)=I1 cos 1sin t =Ismsin t
Where IsmI1 cos 1The utility must supply a small extra amount of current for the capacitor leakage andConverter switching losses in addition to the real power of the load. The total peak
current supplied by the source, is
Isp=Ism+Isl (6)
Where Isl is the peak value of loss current. If the active filter provides the total
reactive and harmonic power, then iS )t( will be purely sinusoidal and in phase with the
utility voltage. At this time, the active filter must provide the following compensationcurrent
ift=iLt-is(t) (7)The desired source currents, after compen sation, can be given
isa*
=Isp sint (8)
isb* =Isp sin (t-120)isb*
=Isp sin (t+120)
Where Isp=Ism+Isl is the amplitude of the desiredsource current, while the phase
angle can be obtained from the source voltages [3]. This peak value of the reference
current has been estimated by regulating the DC side capacitor voltage of the PWMconverter.
3. REVIEW OF FUZZY LOGIC CONTROL
Fuzzy logic control is a control algorithm established upon a linguistic controlscheme, which is derived from expert knowledge into an automatic control strategy.
Fuzzy logic control doesn't need any difficult mathematical calculation like the otherscontrol system. It only uses simple mathematical calculation to simulate the expert
knowledge,whereas the others control system use difficult mathematical calculation
to provide a model of the controlled plant . Although it doesn't need any difficult
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mathematical calculation, but it can provide good performance in a control system.Therefore, it can be one of the best accessible answers today for a broad class of hard
controls problems. A fuzzy logic control usually contains of the following:
Fig.2.Block diagram of FLC
Fuzzification Inference Defuzzification
The above items are given below:
A. Fuzzification
The fuzzy logic controller requires that each input/outputvariable which define thecontrol surface be expressed in fuzzy set notations using linguistic levels. The
linguistic values of each input and output variables divide its universe of discourseinto adjacent intervals to form the membership functions. The member value means
the amount to which a variable belong to a particular level. The process of convertinginput/output variable to linguistic levels is termed as fuzzification.
B. InferenceThe behavior of the control surface which relates the input and output variables of the
system is stated by a set of rules. A standard rule would beIf x is A Then y is B
When a set of input variables are read each of the rule that has any degree of truth in
its premise is fired and contributes to the forming of the control surface byapproximately modifying it. When all the rules are fired, the resulting control surface
is stated as a fuzzy set to represent the constraints output. This process is named asinference.
C. Defuzzification
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The process of conversion of fuzzy quantity into crisp quantity isDefuzzification.There are several procedures available for defuzzification. The essentiallywidespread
one is centroid method, which uses the following formula:
xxdx xdx
where is the membership degree of output x.Figure 2 show block diagram of a fuzzy logic controller (FLC) .
.4 FUZZY LOGIC CONTROL SCHEME
Fuzzy logic control is derived from fuzzy set theory introduced by Zadeh in 1965. In
fuzzy set theory, the transition between membership and non-membership can begradual. Therefore, boundaries of fuzzy sets can be unclear and indeterminate andmaking it useful for approximate systems. FLCs are an interesting choice when
precise mathematical formulations are not possible.In order to implement the control
algorithm of a shunt active power filter in closed loop, the DC side capacitor voltageis sensed and then compared with a reference value. The obtained error
and that are used as inputs for thefuzzy processing.
Where is reference dc side capacitor voltage and ce(n) is Change of error signalat the sampling instant. Figure3 shows the control strategy.The output of the fuzzy controller after a limit is considered as the amplitude of the
reference current Imax.(figure 4 )
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Fig.3.Closed Loop Fuzzy Logic Controlled Shunt
Fig 4. Internal Structure of Fuzzy controller
This current Imaxlook afterthe active power demand of load and the losses in the
system.The switching signal for the PWM converter are obtained from comparing the
actual source currents ( ) with the reference current templates( ina hysteresis current controller. The output pulses are applied to the switching devices
of the PWM converter.
.4.1 Basic Fuzzy Algorithm
The error e and change of error ce are used numerical variables from the real system.To convert these numerical variables into linguistic variables, the following seven
fuzzy levels or sets are chosen as : NB (negative big), NM (negative medium), NS (negative small), ZE (zero), PS
(positive small), PM (positive medium), and PB (positive big) as shown infigures
5(a), 5(b).
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The fuzzy cont olleri descri ed as follows:
Seven fuzzy sets for each input and output
Fuzzification using continuous universe of discourse.
Implication using Mamdani s 'min' operator.
Defuzzification using the 'centroid' method.
.Fig. 5(a).Input Normalized Membership Function
Fig. 5(b). Output Normalized Membership Function
4.2 Rul B :
As shown in Table 1,the elements of this rule base table are adjusted based on the
theory that in the transient state, large errors need coarse control, which requires
coarse input/output variables and in the steady state, small errors need fine control,
which requires fine input/output variab
les. Consequently, withe&ceasinputs theelements ofthe rule table are obtained.
T bl -Control rule base
5. HYSTERSIS B CURRE T TROL
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The hysteresis current control with fixed band can be implemented to generate the
switching pattern in order to get precise and quick response. The hysteresis bandcurrent control technique has proven to be most suitable for all applications of currentcontrolled voltage source inverters in APF[15].
A hysteresis current controller is implemented with a closed loop control system. Anerror signal, e(t), is used to control the switches in an inverter.
This error is the difference between the desired current,iref(t) and the current being
injected by the inverter, iactual(t). When the error reaches an upper limit, the
transistors are switched to force the current down. When the error reaches a lower
limit the current is forced to increase. The range of the error signal, emax-emin, directly
controls the amount of ripple in the output current from the PWM-voltage sourceinverter.
.6 SIMULATION AND RESULTS
Simulation is done basedon the test system used to perform the analysis (figure 6)and system parametersgiven in table 2 to demonstrate the effectiveness of the
proposed control strategy for the SAF to reduce the harmonics. The test systemconsists of a three phase voltage source, and an uncontrolled rectifier with RL load.The active filter is connected to the test system through an inductor Lfand Capacitor
Cfand resistance Rf.The Matlab/Simulink is used to simulate the test power systemwith and without the proposed SAF.
Lf
Rf
Cf
if
iL
C
Fig.6.Test Power System
Ta le 2.Circuit parameters used for the SAF
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Fig .7.Source Voltages
Fig .8(a).Source Currents withoutSAP
Fig.8(b).Harmonic spectrum of phase a source current withoutSAF
Fig.9(a).Source Currents with SAP
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Fig.9(b).FilterCurrents with SAP
Fig.9(c).Load Currents with SAP
Fig.9(d).PowerFactor with SAP
Fig.9(e). DC capacitorvoltage
Fig.9(f).Harmonic spectrum of phase a source current with SAF
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Ta
le 3. Total harmonic distortion Analysis of Test Power System & power factor
PARAMETERSSource Current
(phase a)
THD%
Power FactorReactive
Power(var)
Without SAF 16.83 0.8836 1756
With SAF 1.27 0.9998 98.9
7. CONCLUSIONS
Harmonic distortion is a kind of electrical noise. It is a superposition of signals, which are
of multiples of fundamental frequency. Growth of large power electronic systems results in
increased harmonic distortion. Harmonic distortion results in reduction of power quality and system
stability.This paper presents fuzzy control proper for active power filter for three-phase systems,
which are consisted of nonlinear loads. The SAF was simulated and its performance was analyzed
in a sample power system. The result(figures 7-9 and table3)of the simulation prove that the
injected harmonics are importantly reduced, system efficiency and power factor are improved.
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