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Leonardo Electronic Journal of Practices and Technologies
ISSN 1583-1078
Issue 18, January-June 2011
p. 49-64 [
49 http://lejpt.academicdirect.org
Adaptive Neuro-Fuzzy Inference System based DVR Controller Design
Samira DIB, Brahim FERDI*, Chellali BENACHAIBA
Faculty of the sciences and technology, Department of Technology, Bechar University
B.P 417 BECHAR (08000), Algeria E-mail: [email protected]
* Corresponding author: Phone: (213) 0558111227
Received: 8 October 2010 / Accepted: 13 May 2011 / Published: 25 June 2011
Abstract
PI controller is very common in the control of DVRs. However, one
disadvantage of this conventional controller is its inability to still working
well under a wider range of operating conditions. So, as a solution fuzzy
controller is proposed in literature. But, the main problem with the
conventional fuzzy controllers is that the parameters associated with the
membership functions and the rules depend broadly on the intuition of the
experts. To overcome this problem, Adaptive Neuro-Fuzzy Inference System
(ANFIS) based controller design is proposed. The resulted controller is
composed of Sugeno fuzzy controller with two inputs and one output.
According to the error and error rate of the control system and the output data,
ANFIS generates the appropriate fuzzy controller. The simulation results have
proved that the proposed design method gives reliable powerful fuzzy
controller with a minimum number of membership functions.
Keywords
Dynamic Voltage Restorer (DVR); Sugeno Fuzzy Controller; Adaptive
Neuro-Fuzzy Inference System (ANFIS); Voltage Sag; Voltage Swell;
Voltage Unbalance.
Adaptive Neuro-Fuzzy Inference System based DVR Controller Design
Samira DIB, Brahim FERDI and Chellali BENACHAIBA
50
Introduction
Due to the increased use of a large numbers of sophisticated electrical and electronic
equipment, such as computers, programmable logic controllers, variable speed drives, and so
forth, proliferation of highly sensitive end-user devices is starting to draw attention of both
end customers and suppliers to the question of power quality [1]. Faults at either the
transmission or distribution level may cause voltage sag or swell in the entire system or a
large part of it. Also, under heavy load conditions, a significant voltage drop may occur in the
system. Voltage sags can occur at any instant of time, with amplitudes ranging from 10 - 90%
and a duration lasting for half a cycle to one minute [2]. Further, they could be either balanced
or unbalanced, depending on the type of fault and they could have unpredictable magnitudes,
depending on factors such as distance from the fault and the transformer connections. Voltage
swell, on the other hand, is defined as a sudden increasing of supply voltage up 110% to
180% in RMS voltage at the network fundamental frequency with duration from half a cycle
to 1 minute [2]. Voltage swells are not as important as voltage sags because they are less
common in distribution systems. Voltage sag and swell can cause sensitive equipment (such
as found in semiconductor or chemical plants) to fail, or shutdown, as well as create a large
current unbalance that could blow fuses or trip breakers. These effects can be very expensive
for the customer, ranging from minor quality variations to production downtime and
equipment damage [3]. There are many different methods to mitigate voltage sags and swells,
but the use of a DVR is considered to be the most cost efficient method [3].
The most common choice for the control of the DVR is the so called PI controller
since it has a simple structure and it can offer relatively a satisfactory performance over a
wide range of operation. The main problem of this simple controller is the correct choice of
the PI gains and the fact that by using fixed gains, the controller may not provide the required
control performance, when there are variations in the system parameters and operating
conditions. To solve these problems fuzzy logic control appears to be the most promising, due
to its lower computational burden and robustness. Also, a mathematical model is not required
to describe the system in fuzzy logic based design. But, the main problem with the
conventional fuzzy controllers is that the parameters associated with the membership
functions and the rules depend broadly on the intuition of the experts. If it is required to
change the parameters, it is to be done by trial and error only. There is no scientific
Leonardo Electronic Journal of Practices and Technologies
ISSN 1583-1078
Issue 18, January-June 2011
p. 49-64
51
optimization methodology inbuilt in the general fuzzy inference system [4]. To overcome this
problem, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. In ANFIS, the
parameters associated with a given membership function are chosen so as to tailor the
input/output data set.
This paper introduces Dynamic Voltage Restorer (DVR) and its operating principle,
also presents the proposed Sugeno fuzzy controller which was generated using ANFIS
method design. Then, simulation results using MATLAB-SIMULINK were illustrated and
discussed.
Dynamic Voltage Restorer (DVR)
A Dynamic Voltage Restorer (DVR) is a series connected solid state device that
injects voltage into the system in order to regulate the load side voltage. The DVR was first
installed in 1996 [4]. It is normally installed in a distribution system between the supply and
the critical load feeder. Its primary function is to rapidly boost up the load-side voltage in the
event of a disturbance in order to avoid any power disruption to that load [6]. There are
various circuit topologies and control schemes that can be used to implement a DVR [7, 8]. In
addition to its main task which is voltage sags and swells compensation, DVR can also added
other features such as: line voltage harmonics compensation, reduction of transients in voltage
and fault current limitations [9]. The general configuration of the DVR consists of a voltage
injection transformer, an output filter, an energy storage device, Voltage Source Inverter
(VSI), and a Control system as shown in Figure 1.
Figure 1. DVR general configuration
Adaptive Neuro-Fuzzy Inference System based DVR Controller Design
Samira DIB, Brahim FERDI and Chellali BENACHAIBA
52
Voltage Injection Transformer
The basic function of this transformer is to connect the DVR to the distribution
network via the HV-windings and couples the injected compensating voltages generated by
the voltage source converters to the incoming supply voltage. The design of this transformer
is very crucial because, it faces saturation, overrating, overheating, cost and performance. The
injected voltage may consist of fundamental, desired harmonics, switching harmonics and dc
voltage components. If the transformer is not designed properly, the injected voltage may
saturate the transformer and result in improper operation of the DVR [10].
Output Filter
The main task of the output filter is to keep the harmonic voltage content generated by
the voltage source inverter to the permissible level (i.e. eliminate high frequency switching
harmonics).It has a small rating approximately 2% of the load VA [11].
Voltage Source Inverter
A VSI is a power electronic system consists of switching devices (IGCTs, IGBTs,
GTOs), which can generate a sinusoidal voltage at any required frequency, magnitude, and
phase angle. In the DVR application, the VSI is used to temporarily replace the supply
voltage or to generate the part of the supply voltage which is missing [12].
DC Energy Storage Device
The DC energy storage device provides the real power requirement of the DVR during
compensation. Various storage technologies have been proposed including flywheel energy
storage [13], super-conducting magnetic energy storage (SMES) [14] and Super capacitors
[15, 16]. These have the advantage of fast response. An alternative is the use of lead-acid
battery [17, 18]. Batteries were until now considered of limited suitability for DVR
applications since it takes considerable time to remove energy from them [19]. Finally,
conventional capacitors also can be used [20, 21].
Control System
The aim of the control system is to maintain constant voltage magnitude at the point
Leonardo Electronic Journal of Practices and Technologies
ISSN 1583-1078
Issue 18, January-June 2011
p. 49-64
53
where a sensitive load is connected, under system disturbances. The control system of the
general configuration typically consists of a voltage correction method which determines the
reference voltage that should be injected by DVR and the VSI control which is in this work
consists of PWM with PI controller. The controller input is an error signal obtained from the
reference voltage and the value of the injected voltage (Figure 2). Such error is processed by a
PI controller then the output is provided to the PWM signal generator that controls the DVR
inverter to generate the required injected voltage.
Figure 2. Classical PI controller
Operating Principle of DVR
The basic function of the DVR is to inject a dynamically controlled voltage Vinj
generated by a forced commutated converter in series to the bus voltage by means of a voltage
injection transformer. The momentary amplitudes of the three injected phase voltages are
controlled such as to eliminate any detrimental effects of a bus fault to the load voltage VL.
This means that any differential voltages caused by disturbances in the ac feeder will be
compensated by an equivalent voltage. The DVR works independently of the type of fault or
any event that happens in the system. For most practical cases, a more economical design can
be achieved by only compensating the positive and negative sequence components of the
voltage disturbance seen at the input of the DVR (because the zero sequence part of a
disturbance will not pass through the step down transformer which has infinite impedance for
this component).
The DVR has two modes of operation which are: standby mode and boost mode. In
standby mode (Vinj=0), the voltage injection transformer’s low voltage winding is shorted
through the converter. No switching of semiconductors occurs in this mode of operation,
because the individual inverter legs are triggered such as to establish a short-circuit path for
the transformer connection. The DVR will be most of the time in this mode. In boost mode
(Vinj>0), the DVR is injecting a compensation voltage through the voltage injection
transformer due to a detection of a supply voltage disturbance.
Adaptive Neuro-Fuzzy Inference System based DVR Controller Design
Samira DIB, Brahim FERDI and Chellali BENACHAIBA
54
Voltage Reference Calculation Method
There are lots of methods for DVR voltage correction generating reference voltage
that DVR must inject it into the bus voltage [22-27]. The strategy of voltage reference
calculation used in this work is shown in Figure 3.
Figure 3. SIMULINK model of SRF method for voltage reference calculation
Figure 3 shows the basic control scheme and parameters that are measured for control
purposes. When the supply voltage is at its normal level the DVR is controlled to reduce the
losses in the DVR to a minimum. When voltage sags/swells are detected, the DVR should
react as fast as possible and inject an ac voltage into the grid. It can be implemented using the
synchronous reference frame (SRF) technique based on the instantaneous values of the supply
voltage. The control algorithm produces a three phase reference voltage to the PWM inverter
that tries to maintain the load voltage at its reference value. The voltage sag/swell is detected
by measuring the error between the d-voltage of the supply and the d-reference value. The d-
reference component is set to a rated voltage. The MATLAB/Simulink environment is a
useful tool to implement this method (SRF) because it has many tool boxes that can be used
easily. The SRF method can be used to compensate all type of voltage disturbances, voltage
sag/swell, voltage unbalance and harmonic voltage, but in this work we have studied only
voltage sag/swell. The difference between the reference voltage and the injected voltage is
applied to the VSI to produce the load rated voltage, with the help of pulse width modulation
(PWM) through the PI controller.
Leonardo Electronic Journal of Practices and Technologies
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Issue 18, January-June 2011
p. 49-64
55
ANFIS based controller design
This section introduces the basics of ANFIS network architecture and its hybrid
learning rule. Inspired by the idea of basing the fuzzy logic inference procedure on a feed
forward network structure, Jang [29] proposed an Adaptive Network-based Fuzzy Inference
System (ANFIS) or semantically equivalently, the Adaptive Neural Fuzzy Inference System,
whose architecture is shown in Figure 4. He reported that the ANFIS architecture can be
employed to model nonlinear functions, identify nonlinear components on-line in a control
system, and predict a chaotic time series.
It is a hybrid neuro-fuzzy technique that brings learning capabilities of neural
networks to fuzzy inference systems. The learning algorithm tunes the membership functions
of a Sugeno-type Fuzzy Inference System using the training input-output data. The ANFIS is,
from the topology point of view, an implementation of a representative fuzzy inference
system using a back propagation (BP) neural network-like structure. It consists of five layers.
The role of each layer is briefly presented as follows: let denote the output of node i in
layer l, and xi is the ith input of the ANFIS, i = 1, 2,...,p. In layer 1, there is a node function M
associated with every node:
)x(MO ii1i = (1)
The role of the node functions M1, M2 ...Mq here is equal to that of the membership
functions μ(x) used in the regular fuzzy systems, and q is the number of nodes for each input.
Gaussian shape functions are the typical choices. The adjustable parameters that determine the
positions and shapes of these node functions are referred to as the premise parameters. The
output of every node in layer 2 is the product of all the incoming signals:
)x(M AND)x(MO jjii2i = (2)
Each node output represents the firing strength of the reasoning rule. In layer 3, each
of these firing strengths of the rules is compared with the sum of all the firing strengths.
Therefore, the normalized firing strengths are computed in this layer as:
∑=
i2i
2i3
i OOO (3)
Layer 4 implements the Sugeno-type inference system, i.e., a linear combination of the
input variables of ANFIS, x1, x2, ...xp plus a constant term, c1, c2, ...,cp, form the output of each
IF −THEN rule. The output of the node is a weighted sum of these intermediate outputs:
Adaptive Neuro-Fuzzy Inference System based DVR Controller Design
Samira DIB, Brahim FERDI and Chellali BENACHAIBA
56
∑ =+=
p
1j jjj3i
4i cxPOO (4)
where parameters P1, P2, ...,Pp and c1,c2, ...,cp, in this layer are referred to as the consequent
parameters. The node in layer 5 produces the sum of its inputs, i.e., defuzzification process of
fuzzy system (using weighted average method) is obtained:
∑= i4i
5i OO (5)
The flowchart of ANFIS procedure is shown in Figure 5. ANFIS distinguishes itself
from normal fuzzy logic systems by the adaptive parameters, i.e., both the premise and
consequent parameters are adjustable. The most remarkable feature of the ANFIS is its hybrid
learning algorithm. The adaptation process of the parameters of the ANFIS is divided into two
steps. For the first step of the consequent parameters training, the Least Squares method (LS)
is used, because the output of the ANFIS is a linear combination of the consequent
parameters. The premise parameters are fixed at this step. After the consequent parameters
have been adjusted, the approximation error is back-propagated through every layer to update
the premise parameters as the second step. This part of the adaptation procedure is based on
the gradient descent principle, which is the same as in the training of the BP neural network.
The consequence parameters identified by the LS method are optimal in the sense of least
squares under the condition that the premise parameters are fixed [30].
Figure 4. Structure of ANFIS
Leonardo Electronic Journal of Practices and Technologies
ISSN 1583-1078
Issue 18, January-June 2011
p. 49-64
57
Figure 5. ANFIS procedure
The MATLAB/SIMULINK implementation of the Sugeno fuzzy controller for one
phase is shown in figure 6.
Figure 6. SIMULINK model of the proposed controller
Simulation Results and Discussions
The input-output data pairs for training the ANFIS were generated using the
conventional PI controller. ANFIS structure with Sugeno model containing 9 rules (Tables I)
have been considered. Hybrid learning algorithm method was used to adjust the parameter of
membership function. All the variables’ fuzzy subsets for the inputs ε and ∆ε are defined as
Adaptive Neuro-Fuzzy Inference System based DVR Controller Design
Samira DIB, Brahim FERDI and Chellali BENACHAIBA
58
(M1, M2, M3) with triangular membership function. The membership functions and initial
universes of the inputs generated by ANFIS training are illustrated in Figure 7 and 8. The
output variable Y given by ANFIS training is a vector of constants. Y= [y1, y2, y3, y4, y5, y6,
y7, y8, y9] where, y1=-1397, y2=-1397, y3=-1397, y4=-38.58, y5=-38.58, y6=-38.58, y7=1319,
y8=1320, y9=1320. The control rules are illustrated in Table 1.
Figure 7. Membership function curves of the inputs ε
Figure 8. Membership function curves of the input ∆ε
Table 1. Fuzzy control rules
ε / ∆ε M1 M2 M3 M1 y1 y2 y3 M2 y4 y5 y6 M3 y7 y8 y9
A DVR is connected to the system through a series transformer with a capability to
insert a maximum voltage of 80% of the phase to ground system voltage. In the following
simulations, the main characteristics of the DVR are set as: voltage source full-bridge IGBT
Leonardo Electronic Journal of Practices and Technologies
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59
based inverter controlled with PWM signal generator with commutation frequency of 12kHz,
capacitor energy storage bank 8.8 mF, coupling transformer ratio 1:1, nominal dc link voltage
850V, LC output filter values C=80 µF in series with a damping resistance Rd = 0.1 Ω, L = 1
mH, source voltage 220Vrms and source frequency of 50 Hz. The load is 80 kVA with 0.92
p.f., lagging. The tuning of the PI is made such to have high transient speed and to have very
low tracking error for the fundamental (50 Hz), with Kp = 250 and Ki = 135.
A case of Three-phase 50% balanced voltage sag is simulated and the result is shown
in Figure 9. Voltage sag is initiated at 200 ms and it is kept until 300 ms, with total voltage
sag duration of 100ms. As a result of the control of DVR by the proposed fuzzy controller; the
load voltage is kept at 1.00 p.u throughout the simulation, including the voltage sag period.
We can notice that during normal operation, the DVR is doing nothing but once voltage sag is
detected, it quickly injects necessary voltage components to smooth the load voltage.
0.15 0.2 0.25 0.3 0.35-400
-200
0
200
400S O U R C E V O L T A G E S
V s
(
V )
0.15 0.2 0.25 0.3 0.35-400
-200
0
200
400I N J E C T E D V O L T A G E S
V i
n j
( V
)
0.15 0.2 0.25 0.3 0.35-400
-200
0
200
400L O A D V O L T A G E S
V L
(
V )
T I M E ( s )
Figure 9. Simulation result of DVR response to a balanced voltage sag
Adaptive Neuro-Fuzzy Inference System based DVR Controller Design
Samira DIB, Brahim FERDI and Chellali BENACHAIBA
60
0.15 0.2 0.25 0.3 0.35-500
0
500S O U R C E V O L T A G E S
V s
(
V )
0.15 0.2 0.25 0.3 0.35-400
-200
0
200
400I N J E C T E D V O L T A G E S
V i
n j
( V
)
0.15 0.2 0.25 0.3 0.35-500
0
500L O A D V O L T A G E S
V L
(
V )
T I M E ( s )
Figure 10. Simulation result of DVR response to a balanced voltage swell
0.15 0.2 0.25 0.3-500
0
500S O U R C E V O L T A G E S
V s
(
V )
0.15 0.2 0.25 0.3-400
-200
0
200
400I N J E C T E D V O L T A G E S
V i
n j
( V
)
0.15 0.2 0.25 0.3-500
0
500L O A D V O L T A G E S
V L
(
V )
T I M E ( s )
Figure 11. Simulation result of DVR response to unbalanced voltages
Leonardo Electronic Journal of Practices and Technologies
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p. 49-64
61
For the case of balanced voltage swell compensation represented by Figure 10, the
load voltage is kept at the nominal value with the help of the DVR. Similar to the case of
voltage sag, the DVR reacts quickly to inject the appropriate voltage component (negative
voltage magnitude) to correct the supply voltage.
At the end Three-phase unbalanced voltages condition is investigated to confirm the
performance of the DVR under the proposed fuzzy controller. According to figure 11, the
DVR is able to produce the required voltage components for different phases rapidly and help
to maintain a balanced and constant load voltage at 1.00pu. As a result, the performance of
DVR under the proposed fuzzy controller in mitigating voltage sags/swell and voltage
unbalance is evident. In addition the proposed fuzzy controller has the minimum number of
membership functions for the inputs (3) and do not need gains which make its implementation
practically very easy with a minimum cost.
Conclusions
DVRs are effective custom power devices for voltage sags and swells mitigation.
They inject the appropriate voltage component to correct rapidly any anomaly in the supply
voltage to keep the load voltage balanced and constant at the nominal value. In the present
paper a reliable controller with high performance for dynamic voltage restorers was proposed.
The proposed controller is generated by ANFIS training according to a given input output
data. Compared to the traditional fuzzy controller, the proposed one is the simplest (9 rules
only) and the most cost efficient controller. In addition this controller has no gains to adjust
and solve the problem of traditional fuzzy controller gains tuning.
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