DVR with Artificial Intelligent Controller for Voltage Sag Mitigation

Abstract----Dynamic Voltage Restorer (DVR) is used in power
distribution system to protect sensitive load in voltage disturbances.
Voltage sag is one of the most important power quality problems
challenging the utility industry. Voltage sags can be compensated
by voltage and power injection into the distribution system. DVR is
developed using Mamdani-type fuzzy inference system and
Sugeno-type fuzzy inference system. They are two input and one
output. Both Mamdani-type fuzzy inference system and Sugeno-
type fuzzy inference system are simulated using MATLAB fuzzy
logic toolbox. This paper outlines the basic difference between
these two fuzzy inference system and their simulated results were
compared.
Keywords‫ـــــ‬Dynamic Voltage Restorer (DVR), Mamdani-type
fuzzy inference system (FIS), MATLAB, Sugeno-type fuzzy
inference system (FIS), and Voltage Sag
I. INTRODUCTION
N recent years the demand for quality of electronic power
has been increasing rapidly. Power quality problems has
recieved a great attention nowadays because of their
impacts on both utilites and customers[1]. There are many
power quality problems such as voltage sag, voltage swell,
harmonics, flickers, interruption etc. Among these, voltage
sag is considered to have the most severe effect on the
equipments[2]. These effects can be very burdening for
customers, from minor quality variations to production
downtime and equipment damage[3].
Mohamed Mohamed Khaleel is currently working toward the MSc degree
in Sebelas Maret University, Central Java,Indonesia. His research interest
includes Power System Analysis, Power Electronics ,Power Quality,Control
Systems,Simulation, FACTs and Renewable Energy. Email:
lykhaleel@gmail.com
Khaled Abduesslam is currently working toward the MSc degree in
Sebelas Maret University, Central Java,Indonesia. His research interest
includes Power System Analysis, Power Electronics ,Power Quality,Control
Systems, and Renewable Energy.
Prof. Muhammad Nizam, Member of University Senate / Head of
control unit UNS Ex. Power System. Power Quality. Energy Management
System. Email: Nizam_kh@ieee.org
Dr. Inayati, ST., MT., ph.D, Chemical Engineering Department, Faculty
of Engineering, Sebelas Maret University, Surakarta, Indonesia. Telp:
+62271632112 (office) +62274375213 (home) +6282138248908 (mobil[[)
Email: inayati@fi.uns.ac.id
According to the IEEE standard,voltage sag decreases
from 0.1 to 0.9 p.u in the rms voltage level at the system
frequency and with duration of half cycle to 1 min[4].
Voltage swells are not considered as important as voltage
sag because they are less common in distribution systems
[3].
DVR is a custom power device connected to the load
through a series transformer. To compensate voltage
distrubances, series voltage is injected through the
transformer by voltage-source converter connected to dc
power source. The first DVR was intalled in North Carolina,
for the rug manufacturing industry. Another was intalled to
provide service to a large dair food processing plant in
Australia [16].
There are many different methods using to mitigate
voltage sag. Using a DVR method is an effective Custom
Power Device which can improve power quality, especially
voltage sags [4]. The basic operating principle of DVR is to
detect the voltage sag and inject the missing voltage in series
transformer [2].
Artificial Intelligence techniques such as neural networks
and Fuzzy Logic Controller (FLC) are getting more attention
nowadays. d-q-0 technique has been proposed based on
Artificial Intelligent techniques. Both Mamdani-type fuzzy
inference system and Sugeno-type fuzzy inference system
are two samples of methods proposed for the DVR The
advantage of these two techiques is that they use human
experience. In addition. FLC appears to be the most
promising due to its lower computation burden and
robustness.
This paper not only introduces DVR and its operating
principle, but also presents the proposed controller which is
a combination of FLC and d-q-0 technique. DVR control
approaches used in this paper are Mamdani-type fuzzy
inference system and Sugeno-type fuzzy inference system
methods. These the two approaches are going to be
compared to determine which approach is more robust.
Then, the simulation results are going to be analyzed using
MATLAB-SIMULINK to provide a comparison between the
proposed and the conventional d-q-0 technique in terms of
performance in voltage sag compensation.
II. MODELLING OF DVR
Among power quality problems (voltage sag and swell,
harmonics), voltage sag causes the most severe distrubances.
In order to overcome these disturbances, the concept of
custom power devices are introduced. One of those device is
I
DVR with Artificial Intelligent Controller for
Voltage Sag Mitigation
Mohamed Mohamed Khaleel, Khaled Abduesslam, Prof. Muhammad Nizam,
and Dr. Inayati, ST., MT., ph.D
SEBELAS MARET UNIVERSITY,INDONESIA
International Conference on Advances in Engineering and Technology (ICAET'2014) March 29-30, 2014 Singapore
http://dx.doi.org/10.15242/IIE.E0314165 557

DVR, which is regarded as the most efficient and effective
modern custom power device used in power distribution
networks.
The function of the DVR is to inject the missing voltage
in order to regulate the load voltage from any disturbance
due to immediate distort of source voltage [5]. The DVR
was first intalled in 1996. DVR is normally installed in
distribution between the supply and critical load feeder.
Other DVR functions are to rapidly boost up the loadside
voltage in the event of a disturbance and avoid any power
disruption to that load there arevarious circuit topologies and
control schemes [6]. DVR can also add other features like
line voltage harmonics compensation, reduction of transients
in voltage and fault current limitations[5].
The general configuration of DVR consists of voltage
injection transformer, an output filter, an energy storage
device, voltage Source Inverter (VSI), and Control system
shown in Figure 1.
Fig. 1 Structure of DVR [6]
DVR is a series connected device designed to maintain
aconstant RMS voltage across a senstive load. The DVR
consists of :
A. Voltage Source Inverter (VSI)
Voltage Sourse Inverter converts the DC voltage from the
energy storage unit to a controllable three-phase AC voltage.
The inverter switches are normally fired using a sinusiodal
pulse Width Modulation scheme[7].
B. Passive Switch
Filters are used to convert the inverted PWM pulse
waveform. This is acheived by removing the unnecessary
higher harmonic compounds generated during the DC to AC
conversion in the Voltage Source Inverter. Higher harmonic
componeents distort the compensated output voltage [8].
C. Energy Storage Unit
It is important for energy to be stored in the DC form
such as flywheels, batteries, superconducting magnetic
energy storage (SMES) and super capacitors.They can be
used as energy storage devices and supply the real power
requirement of the system when DVR used for
compensation[9].
D. Voltage Injection Transform
In a three-phase system, three single-phase transformer units
and one-three phase transformer unit can be used inject the
voltage in the proposed system [10].
E. Control System
The aim of the control system is to maintain constant
voltage magaitude at the point where a senstive load is
connected under system disturbances [11].
III. OPERATION OF DVR
DVR dynamically injecs controlled voltage in series with
bus voltage through the booster tranformer. The amplitudes
of the injected phase voltages are controlled to eliminate the
terimental effects of the bus fault to the load voltage. The
system impendance Zth depends on the fault level of the load
bus when the system voltage (Vth) drops. DVR injects a
series voltage VDVR through the injection of tranformer so
that the desired load voltage magnitude VL can be
maintained. The series injected voltage on DVR can be
written as [14] :
V DVR = VL+ Zth Ith – Vth (1)
IL = (PL+J QL / VL ) (2)
VL =Desired load voltage magnitude
Zth = load current
IL = load current
Vth= system voltage during fault
IV. PARK’S TRANSFORMATION
DVR control employs a-b-c to d-q-0 transformation.
During normal condtion and symmetrical condition, the
voltage will be constant and d-voltage is unity in p.u and q-
voltage is zero in p.u. but during the abnormal conditions,
the voltage change. After comparing d-voltage and q-
voltage with the desired voltage error, d and q are
generated. This error component is converted into a-b-c
compent using d-q-0 to a-b-c transformation. Phase locked
loop (PLL) is used to generate unit sinusoidal wave in phase
with main voltage. These a-b-c components are given to
generate three-phase pulses using Width Modulation
(PWM) technique[13].
Fig. 2 Scheme Control d-q-0

Figure 2 illustrates the basic control scheme and
parameters measured for control purposes. When the supply
voltage is at its normal level, DVR is controlled to reduce
the losses in the DVR to a minimum voltage. When voltage
sags/swells are detected, DVR will react fast and inject an
AC voltage into the grid.
The d-q-0 transformation, Park’s transformation is used to
control of DVR. The d-q-0 method gives the sag depth and
phase shift information when the voltage sag starts and when
the voltage ends. The quantities are expressed as the
instantaneous space vectors. First, convert the voltage from
a-b-c reference frame to d-q-0 reference.
(1)
Equation 1 defines the transformation from three-phase
system a, b, c to d-q-0 stationary frame. In this
transformation, phase A is aligned to the d axis that is in
quadrature with the q-axis .The theta (θ) is defined by the
angle between phases A to the d-axis. The error signal is
used as a modulation signal generating a commutation
pattern for the power switches (IGBT’s) constituting the
voltage source converter. [15].
V. FUZZY LOGIC CONTROLLER (FLC)
Fuzzy logic controller (FLC) was first proposed in 1965
as a way to imprecise data by Lofti Zadeh, professor at
University of California. After being mostly viewed as a
controversial technology for two decades, FLC has finally
been accepted as an emerging technology since the late
1980s. This is largely due to a wide array of successful
applications such as consumer products, industrial process
control, and automotive applications.
FLC is close in spirit to human thinking and natural
language than conventional logical systems FLC is a
methodology representing and implementing human’s
knowledge about how to control a system [17]. In general,
FLC provides an inference structure enabling appropriate
human reasoning capabilities. Figure 3 shows the basic
configuration of FLC.
Fig. 3 Basic Configuration of FLC
Unlike Boolean logic, FLC allows states (membership
Values) between 0 or 1 [19]. The general structure of FLC is
represented in Figure 4 below. FLC comprises four
principal components:
 a fuzzyfication interface which converts input data into
suitable linguistic values;
 a knowledge base which consists of data base with the
necessary linguistic definitions and control rule set;
 a decision-making logic which stimulates human decision
process inferring the fuzzy control action from
knowledge of the controlrules and linguistic variable
defenitions;
 a defuzzyfication interface which yields a non-fuzzy
control action
In this paper, FLC block are used for error singnal-d and
error signal-q. The error is calculated from the difference
between supply voltage data and reference voltage data. The
error rate is the the change of error. The error can be
formulated as shown in equation below:
Error =Vref –SV (3)
Error rate= error(n)-error (n-1) (4)
Fig. 4 Fuzzy Logic Controller (FLC) Scheme
The aim of the control system is to maintain voltage
magnitude at the point where a sensitive load is connected
under system disturbances. The harmonics is generated in
the load terminals using six pulse converters with fixed
firing angle connected to the main drive non linear load
which is parallel to the sensitive load. Voltage sag is created
at load terminals via a three-phase fault. The above voltage
problems are sensed separately and passed through the
sequence analyzer. The control system of the general
configuration typically consists of a voltage correction
method which determines the reference voltage injected by
DVR. In this paper, VSI consists of PWM with FLC.
Input consists of fuzzy error signal derived from the
reference voltage. It controls the proposed DVR based on a
fuzzy logic. The PWM signal generator controls the DVR
inverter to generate the required injected voltage. The
function of FLC is very useful since its exact mathematical
model is not required. FLC system can be divided into four
main functional blocks; Knowledge base, Fuzzification,

Inference mechanism and Defuzzification Rule base. In this
study, a fuzzy logic based feedback controller is employed to
controll the voltage injection of the proposed (DVR). The
implication step of FLC is used to introduce the evalution of
indiviual rules.
In further sections of this paper, analysis of various
methods of DVR control, each resulting from innovative
improvements on the existing methods will be carried out.
New DVR control approaches introduced in this paper are
Sugeno-type fuzzy inference system and Mamdani-type
fuzzy interference system.
A. Sugeno-type FIS
Sugeno-type fuzzy inference system has better processing
time since the weighted average replaced the time
consuming defuzzification process than Mamdani-type fuzzy
inference system. Due to the interpretable and intuitive
nature of the rule base, Mamdani-type FIS is widely used in
particular for decision support application. Other differences
are that Mamdani-type FIS has output membership functions
whereas Sugeno-type FIS has no output membership
functions and Mamdani-type FIS is less flexible in system
design compared to Sugeno-type FIS as latter can be
integrated with ANFIS tool to optimize the outputs [18].
The knowledge base is composed of data base and rule
base. Data base consists of input and output membership
functions and provides information for appropriate
fuzzification and defuzzification operations. The rule-base
consists of a set of linguistic rules relating the fuzzified input
variables to the desired control actions. Fuzzification
converts a crisp input signal, the error, and error change
(delta error) into fuzzified signals identified by level of
membership in the fuzzy sets. The inference mechanism uses
the collection of linguistic rules to convert the input
conditions to fuzzified output. Finally, the defuzzification
converts the fuzzy outputs to crisp control signals, which in
the system acts as the changes in the control input. Input-
output fuzzy consists of two inputs that error and delta_error
and one output.
Fig. 5 Construction of a FLS file in Matlab
Fig. 6 Membership functions of input error (E).
Fuzzy Interference System constructing processes are
conducted as explained below:
1) Choose a specific type of FIS (Mamdani or sugeno)
2) Select relevant input-output variables
3) Determine the number of linguistic terms associated
with each input-output variables (determine the
membership function for each lingistic term)
4) Design a collections of fuzzy if-then rules.
5) Choose the defuzzification method to exract a crisp
value that best represents fuzzy set.
6) Run through test suite to validate system and adjust
details as required.
Fig. 7 Membership functions of input delta_error.
The applied output of the fuzzy system consists of
constant variables such as NB, NS, Z, PS, PB where NB=-
250, NS=-200 Z=0, PS=200, PB=250. Those variables are
then converted into the fuzzy rule base as shown in the table
1.
TABLE I
FUZZY RULES
Error
delta_error
N Z P
N NB NS Z
Z NS Z PS
P Z PS PB
B. Mamdani-type FIS
Mamdani-type fuzzy inference system method is widely
accepted to capture expert knowledge. It allows us to
describe the expertise in more intuitive and more human-like
manner. However, this method entails a substantial
computational burden. On the other hand, Sugeno-type fuzzy
inference system method is computationally efficient and

works well with optimization and adaptive techniques,
making it very attractive in control problems, particularly for
dynamic non linear systems. These adaptive techniques can
be used to customize the membership functions so that fuzzy
system best models the data. The most fundamental
difference between Mamdani-type and Sugeno-type is the
way the crisp output is generated from the fuzzy inputs.
While Mamdani-type FIS uses the technique of
defuzzification of a fuzzy output, Sugeno-type FIS uses
weighted average to compute the crisp output. The
expressive power and interpretability of Mamdani-type
output is lost in the Sugeno-type since the consequents of the
rules are not fuzzy [18].
Fig. 8 Membership functions of input error
Fig. 9 Membership functions of input delta-error
Fig. 10 Membership functions of output fuzzy set.
Output of the Mamdani-type fuzzy inference system is
linear function [-10000 10000] consisting of a fuzzy set NB,
NS, Z, PS, and PB where, NB = negative big, NS = negative
small, Z = zero, PS = positive small, and PB = positive large.
TABLE II
FUZZY RULES
Error
delta_error
N Z P
N NB NS Z
Z NS Z PS
P Z PS PB
VI. DVR TEST SYSTEM
The performance of the designed DVR, as shown in
Figure 13 and 14 is evaluated using Matlab/Simulink. Tablet
1 shows the values volumes of parameters. DVR Test
System comprises 13 kV,50 Hz generator, feeding
transmissions lines through a 3-winding transformer
connected in Y/Delta/Delta
Fig. 11 Matlab model of the DVR based on d-q-0 technique
Fig. 12 Matlab model of the DVR based on FLC(Sugeno/Mamdani)
TABLE III
SYSTEM PARAMETERS

VII. RESULTS DISCUSSION
The result consists of the simulation of distribution
network without installation of DVR and simulation on
distribution network with installation of DVR systems. DVR
is simulated using MATLAB SIMULINK. The voltage sags
occur at the time duration of 0.4 sec.
1) Control Surfaces using FLS
The plot is obtained after the simulation of Mamdani-type
fuzzy inference system (FIS) and Sugeno-type fuzzy
inference system (FIS) using MATLAB Fuzzy logic toolbox
(as shown in Figures 13 and 14).
Fig. 13 Control Surfaces of Mandani-type fuzzy inference system.
Fig. 14 Control surfaces of Sugeno-type fuzzy control
2) The Result oF DVR
DVR is simulated using MATLAB SIMULINK is used
when stimulating the DVR. From this simulation, it can be
concluded that the voltage sags occur for 0.4 second as
shown in Figure 15 below.
Fig. 15 Compensation of Sag by one-phase d-q-0 with DVR and
without DVR
The orange line indicates the occurrence of voltage sag at
period of time from 0.4 p.u up to 0.8 p.u. The value of the
voltage drops up to 0.14 p.u. because DVR is not installed in
the grid. Meanwhile, the black line indicates the DVR
installation used in the grid, injecting voltage up to 88% at
single phase fault.
Fig. 16. Compensation of Sag by one-phase fuzzy logic Controller (
Sugeno/Mamdani) with DVR and without DVR.
As seen in Figure 16, the green line shows the using
Sugeno-type fuzzy inference system method when the
voltage sag happens at the period of time from 0.4 up to 0.8.
DVR based Sugeno-type is proven to be able to support the
network by 98%. The blue line illustrates the using
Mamdani-type fuzzy inference system method Mamdani-
type fuzzy inference system which gives the same result as
Sugeno-type fuzzy inference system .Meanwhile, the orange
lines indicates when single-phase fault happens without
DVR installation.
Fig. 17 Compensation of Sag by one-d-q-0, fuzzy logic controller
(Sugeno/Mamdani) with DVR and without DVR
As described in Figure 17, the green line illustrates that
the green color indicates voltage sag occurrence at the
period of time from 0.4 up to 0.8. when DVR Sugeno-type
fuzzy inference system is installed. DVR based Sugeno
Fuzzy is proven to be able supporting 98% network in the
grid. The blue line shows that Mamdani-type fuzzy inference
system installation give the same result as Sugeno-type
fuzzy inference system when the voltage happens.
Meanwhile, the orange line illustrates when single-phase
fault happens in the grid without DVR installation. The
black line describesthe injected voltage using DVR based on

d-q-0 technique where park’s tranformation is able to
support 88% network in the grid.
Fig. 18 Compensation of Sag by two-phase d-q-0, fuzzy logic
controller (Sugeno) with DVR and without DVR
Figure 18 illustrates that both of Sugeno-type and
Mamdani-type fuzzy inference system inject the same
value at 96% in the grid when voltage sag happens at the
period of time from 0.4 up to 0.8. In this scheme, DVR
based d-q-0 technique is able to support 84% network in
the grid two-phase fault.
Fig. 19 Compensation of Sag by three-phase d-q-0, Sugeno fuzzy
controller with DVR and without DVR.
Figure 19 illustrates that both of Sugeno-type and
Mamdani-type fuzzy inference system injects the same
value at 94% in the grid when voltage sag happens at the
period of time from 0.4 up to 0.8. In this sceme, DVR based
d-q-0 technique is able to support 80% network in the grid
at three-phase fault.
VIII. CONCLUSION
This paper concludes that DVR is an effective device to
compensate the voltage sag in power distribution systems. In
term of DVR applications, although Mamdani-type and
Sugeno-type share the same functions and rules, there are
some distinctions between them. The output membership
functions in Sugeno-type can only be either constant or
linear. The crisp output is generated in different ways for
both the types. In some ways, Sugeno-type is more superior
than Mamani-type as it can be integrated with neural
networks and genetic algorithm or other optimization
techniques.
IX. REFERENCES
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[18] Ezhilarasan S.,Balasubramanian G., DVR for Voltage Sag Mitigation
Using Pi With Fuzzy Logic Controller, International Journal of
Engineering Research and Application, Vol. 3. 2013
Dr. Inayati, ST., MT., ph.D Chemical Engineering
Department, Faculty of Engineering, Sebelas Maret
University, Surakarta, Indonesia. Telp:
+62271632112 (office) Email: inayati@fi.uns.ac.id
Mohamed Mohamed Khaleel was born in 09-07-
1988, Misrata, Libya. He is currently working
toward the MSc degree in Sebelas Maret
University, Central Java,Indonesia. His research
interest includes Power System Analysis, Power
Electronics ,Power Quality, Control Systems,
Renewable Energy, and Artificial Intelligence
Systems.
Email: lykhaleel@gmail.com
Prof. Muhammad Nizam. M. T. ph., D MIEEE.,
IPM.Member of University Senate /Head of
control unit UNS Ex.Power System. Power
Quality. Energy Management System. Email:
Nizam_kh@ieee.org
Khaled Abduesslam – M was born in 09-07-
1983, Tekarkeba , Libya. He is currently working
toward the MSc degree in Sebelas Maret
University, Central Java,Indonesia. His research
interest includes Power System Analysis, Power
Electronics ,Power Quality,Control Systems, and
Renewable Energy.

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