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International Journal of FuzzySystems ISSN 1562-2479 Int. J. Fuzzy Syst.DOI 10.1007/s40815-018-0491-6
Simulation of Reduced Rating DynamicVoltage Restorer using SRF–ANFISController
R. Bhavani & N. Rathina Prabha
1 23
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Simulation of Reduced Rating Dynamic Voltage Restorer usingSRF–ANFIS Controller
R. Bhavani1 • N. Rathina Prabha1
Received: 26 September 2017 / Revised: 16 March 2018 / Accepted: 5 April 2018
� Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract Power Quality (PQ) and reliability in distribu-
tion system have been appealing to a greater extend in
modern era and also have become an area of concern for
current industrial and commercial applications. This paper
examines the problem of voltage sag and swells and also
deals with the improved design of Dynamic Voltage
Restorer (DVR) for PQ enhancement. A novel control
algorithm Synchronous Reference Frame (SRF) theory
with Adaptive Neuro-Fuzzy Inference System (ANFIS)
controller is proposed for the creation of reference DVR
voltages. In addition, different voltage injection schemes
are analyzed to focus on novel method for the design of
Reduced Rating DVR (RRDVR) to improve its perfor-
mance in terms of output power, cost and size. The pro-
posed DVR is demonstrated for PQ problems sag and swell
using MATLAB/SIMULINK. During compensation, the
output power attained from the proposed DVR is also
compared with other intelligent controllers, namely Fuzzy
Logic (FL) and ANFIS controller. Simulation results
proved that the proposed SRF–ANFIS controller-based
RRDVR offers economic solution for both utilities and
customers by providing extremely deep compensation for
voltage-based PQ problems occurring at very short dura-
tion of time.
Keywords Power quality (PQ) � Sag � Swell � Dynamic
voltage restorer (DVR) � ANFIS controller � Synchronous
Reference Frame (SRF) theory algorithm
1 Introduction
The stability of supply and quality of power are the two
main aspects in power distribution systems. Modernization
and computerization of industry involve increase in use of
power electronic devices which mostly contribute power
quality (PQ) problems [1–3] such as voltage sag, swell,
interruption, harmonic, flickers and impulse transients. So
the demand for high quality of power turns into a vital
issue. At present, voltage sag and swell [4, 5] are recog-
nized as the most frequent and serious threats and have
consequences such as sensitive load tripping, production
loss, malfunction of control equipment, equipment failure
and long-term breakdown of components.
DVR is the modern, most efficient and effective custom
power device used in distribution networks [6, 7]. It can
eliminate most of the voltage-related PQ problems and
minimizes the hazard of load tripping from very deep sag
and swell problems. Many researchers work to find solu-
tions to improve the performance of DVR. Sensitive load
voltage compensation using DVR is discussed in [8].
SEMS-based DVR is demonstrated in [9]. The design of a
self supported DVR for PQ problems is presented in
[10, 11]. The design of Z source matrix converter-based
DVR is demonstrated in [12]. The compensation ability of
DVR depends on its DC input voltage which determines
the magnitude of AC voltage injected during sag event. In
conventional DVR the choice of DC link voltage should be
greater than 1.5 times grid voltage which increases the VA
rating of VSI. This increases size of DVR and capital
investment which restrict its installation for many places. It
is necessary to design low-rating DVR with improved
compensation. This paper deals with the design of reduced
rating DVR (RRDVR) by introducing a new voltage
injection technique to reduce the magnitude of DVR-
& R. Bhavani
N. Rathina Prabha
1 Mepco Schlenk Engineering College, Sivakasi 626005, India
123
Int. J. Fuzzy Syst.
https://doi.org/10.1007/s40815-018-0491-6
Author's personal copy
injected voltage during compensation and consequently
optimizes the energy injected from DVR which indirectly
reduces capacity and size of DVR. Many research works
were presented to reduce the rating of DVR [13, 14].
For compensation, conventional PI controller [15] is
used along with DVR which will provide a comparatively
satisfactory performance over a wide range of operation.
But, the main problem is the accurate selection of PI gains.
To resolve these problems, fuzzy logic control [16] can
turn out to be the most capable one due to its robustness.
But, the difficulty with fuzzy controller is that the mem-
bership functions parameters and the rules depend broadly
on the perception of the experts to be organized by trial and
error only. To overcome this, researchers have used many
different methods over the past decades including different
optimization algorithms, neural networks, adaptive neuro-
fuzzy inference system (ANFIS) and other numerical
techniques. Among these control techniques, ANFIS is
considered as an effective one [17–21] with optimized
fuzzy rules, faster speed of operation without any modifi-
cations in membership functions by conventional trial and
error.
In this paper, a hybrid intelligent SRF–ANFIS controller
is proposed to enhance the performance of RRDVR. SRF
control algorithm is able to detect sag and swell issues with
no error and adds the proper voltage component to correct
instantly any fault in the terminal voltage to keep the
voltage at the load end balanced and constant with nominal
value. The performances of the proposed DVR are tested
for both symmetrical and asymmetrical sag and swell
problems. The effectiveness of the proposed SRF–ANFIS
control algorithm is validated by comparing its output
power with FUZZY and ANFIS controllers.
2 SRF Control Algorithm for DVR
DVR is a solid-state inverter which adds the series voltage
with a controlled magnitude and phase angle to bring back
the quality of the load voltage to the pre-specified value
from PQ problems due to instant deform of source voltage.
It is normally established in a distribution system between
the supply and the critical load feeder which is shown in
Fig. 1.
The control technique proposed for DVR should afford
very deep and efficient compensation for both balanced and
unbalanced sag/swell problems by considering limitations
such as the voltage inserting capability of voltage source
inverter (VSI) and also size of the input capacitor. The flow
diagram for the proposed Synchronous Reference Frame
(SRF) theory for the control of DVR is shown in Fig. 2.
The sensed three-phase terminal voltages are converted
from rotating reference frame to stationary using the abc–
dqo conversion [1] as
VTq
VTd
VT0
24
35¼ 2
3
cosh cos h� 2p3
� �cos hþ2p
3
� �
sinh sin h� 2p3
� �sin hþ2p
3
� �
1=2 1=2 1=2
266664
377775
VTa
VTb
VTc
24
35:
ð1Þ
A phase-locked loop (PLL) is used to synchronize load
and terminal voltages. The magnitude of reference DC bus
voltage is compared with actual DC bus voltage, and the
error is given to PI controller to generate voltage loss
component and is added to Vd to generate Vd* [2]. The
reference d-axis load voltage
V�d � Vsd dc � Vloss: ð2Þ
Fig. 1 DVR-connected system
Fig. 2 Flowchart for SRF control algorithm
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Another PI controller is used to regulate the amplitude
of the load voltage, and its output is considered as the
reactive component of voltage (Vqr) is added with Vq to
generate reference q axis load voltage Vq* [3].
V�q ¼ VTq dc þ Vqr: ð3Þ
At the point of common coupling, the amplitude of load
voltage is computed as [4]
VL ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2
3
� �V2
La þ V2Lb þ V2
Lc
� �s
: ð4Þ
The resultant reference frame voltages are again chan-
ged into a–b–c frame using reverse Park’s transformation
as [5] to generate gating pulses for the switches of DVR.
V�La
V�Lb
V�Lc
24
35 ¼
cos h cos h� 2p3
� �cos hþ 2p
3
� �
sin h sin h� 2p3
� �sin hþ 2p
3
� �
1=2 1=2 1=2
266664
377775
V�Lq
V�Ld
V�L0
24
35
ð5Þ
3 ANFIS Controller
Adaptive neuro-fuzzy inference system (ANFIS) is a
hybrid neuro-fuzzy technique that captures learning capa-
bilities of neural networks to fuzzy inference systems to
provide optimized fuzzy inference system (FIS). The
learning algorithm adjusts the membership functions of a
Sugeno-type FIS utilizing the training input–output data.
ANFIS utilizes the hybrid learning rule and manages
complex decision-making or diagnosis systems. ANFIS has
been proven to be an effective tool for tuning the mem-
bership functions of FIS. The ANFIS architecture is a five-
layer feed-forward network as shown in Fig. 3.
An adaptive network is a multilayer feed-forward net-
work in which each node performs a particular function
(node function) on incoming signals as well as a set of
parameters pertaining to this node. The formulas for the
node functions may vary from node to node, and the choice
of each node function depends on the overall input–output
function which the adaptive network is required to carry
out. To reflect different adaptive capabilities, both circle
and square nodes are used in an adaptive network. A square
node (adaptive node) has parameters, while a circle node
(fixed node) has none. The parameter set of an adaptive
network is the union of the parameter sets of each adaptive
node. In order to achieve a desired input–output mapping,
these parameters are updated according to given training
data and a gradient-based learning procedure is used.
Layer 1 Every node in this layer is a square node with a
node function (the membership value of the premise part)
O1i ¼ lAiðxÞ ð6Þ
where x is the input to the node i, and Ai is the linguistic
label associated with this node function.
Layer 2 Every node in this layer is a circle node labeled
P which multiplies the incoming signals. Each node output
represents the firing strength of a rule.
O2i ¼ lAiðxÞ lBiðyÞ where i ¼ 1:2 ð7Þ
Layer 3 Every node in this layer is a circle node labeled
N (normalization). The ith node calculates the ratio of the
ith rule’s firing strength to the sum of all firing strengths.
O3i ¼ �Wi ¼
Wi
W1 þW2
; where i ¼ 1:2 ð8Þ
Layer 4 Every node in this layer is a square node with a
node function
O4i ¼ �Wifi ¼ �Wi Pixþ Qiyþ Rið Þ; where i ¼ 1:2 ð9Þ
Fig. 3 Architecture of ANFIS controller
Table 1 Rule base representation
e
∆eNB NM NS Z PS PM PB
NB NB NB NB NM NM NS Z
NM NB NB NM NM NS Z PS
NS NB NM NM NS Z PS PM
Z NM NM NS Z PS PM PM
PS NM NS Z PS PM PM PB
PM NS Z PS PM PM PB PB
PB Z PS PM PM PB PB PB
R. Bhavani, N. Rathina Prabha: Simulation of Reduced Rating Dynamic Voltage Restorer using SRF–ANFIS Controller
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Layer 5 The single node in this layer is a circle node
labeled R that computes the overall output as the summa-
tion of all incoming signals
O5i ¼ System output; where i ¼ 1:2: ð10Þ
Equation [10] represents the overall output of the
ANFIS controller.
The set of fuzzy control linguistic rules is given in
Table 1. The inference system of fuzzy logic controller
makes use of these rules to generate the required output.
The training data for ANFIS controller were acquired
using SRF–PI controller for DVR. Fuzzy subset for the
inputs to the ANFIS controller is e and De is described
using seven variables (NB, NM, NS, Z, PS, PM and PB)
where N–negative big, NM—negative medium, NS—
negative small, Z—zero, PS—positive small, PM—posi-
tive medium, and PB—positive big with Gaussian mem-
bership functions). Sugeno-type fuzzy inference system
(FIS) is modeled by constructing 49 rules using seven
linguistic variables for ANFIS controller. Hybrid back-
propagation learning algorithm is used to regulate the
parameter of membership function. The inputs to ANFIS
controller are modeled as
e kð Þ ¼ Vref�Vt ð11ÞDe kð Þ ¼ e kð Þ � e k � 1ð Þ ð12Þ
Fig. 4 Membership functions for a error, b change in error, c output
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where Vref is the reference voltage, Vt is the terminal
voltage. e(k) and De(k) are the error and change in error,
respectively.
The input and output membership functions are shown
in Fig. 4a–c.
A number of epochs chosen are 100 with a training error
of 0.01. The MATLAB simulated structure of the proposed
ANFIS controller shown in Fig. 5 is capable to compensate
both source and load side problems. ANFIS output for
training error is also shown in Fig. 6.
Fig. 5 Simulated ANFIS structure
Fig. 6 Error versus epochs
Fig. 7 Phasor diagram for voltage injection schemes a sag, b swell
R. Bhavani, N. Rathina Prabha: Simulation of Reduced Rating Dynamic Voltage Restorer using SRF–ANFIS Controller
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4 Proposed Compensation Strategy
The proposed DVR should be able to compensate different
types of voltage sag and swell problems frequently
occurring in a three-phase distribution system. During sag
event, DVR injects the required magnitude of voltage such
that the load voltage Vload is constant in magnitude and
undistorted. The phasor diagram of the different voltage
injection scheme of DVR for voltage sag is shown in
Fig. 7a.
VL (pre-sag) is a voltage across the critical load pro-
ceeding to voltage sag. During the voltage sag, the load
voltage is reduced to VL (sag) with a phase lag angle of h.
Now the DVR needs to provide some voltage such that the
load voltage magnitude is maintained at the pre-sag con-
dition. Based on the phase angle of load voltage, the
voltage injected by DVR can be realized in four ways. Vins1
represents the voltage injected by DVR that is in-phase
with the VL (sag). With the injection of Vins2, the load
voltage magnitude retains the same but it leads VL (sag) by
a small angle. In Vins3, the load voltage holds the same
phase as that of the pre-sag condition. Vins4 is the condition
where the injected voltage is in quadrature with the current.
From the above, it is inferred that the DVR injects mini-
mum magnitude of voltage when it is in phase with sag
voltage.
The phasor diagram for DVR-injected voltage during
voltage swell event is shown in Fig. 7b. VLpreswell is the
load voltage prior to voltage swell. During swell event, the
load voltage is increased to VLswell. DVR needs to inject
some voltage (Vabs) in opposite phase by providing lagging
VAR to keep the load voltage constant in magnitude. The
magnitude of DVR-injected voltage depends on injection
angle. From the phasor diagram, it is inferred that the
Fig. 8 DVR-interconnected system with ANFIS controller
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injection of Vabs1 gives the optimum possible rating of
DVR. The proposed DVR is operated in this strategy. Thus,
the very deep PQ problems can be compensated using low-
rating DVR.
5 Performance of the Proposed System
The performance of the proposed DVR is tested by inter-
connecting with distribution system. The distribution sys-
tem is modeled using 415 V, 50 Hz three-phase supply
connected to critical load of 15 MVA, 0.8 PF lagging
through transmission line with line impedance Ls = 3 mH,
Rs = 0.01 X. The SIMULINK model of the DVR-inter-
connected system with ANFIS controller is shown in
Fig. 8. The PQ problems voltage sag and swell are simu-
lated by creating a three-phase fault and adding capacitive
load with the duration of 0.2–0.4 s, respectively.
The MATLAB implementation of SRF–ANFIS control
algorithm is shown in Fig. 9. It is used to generate the
gating signals for the IGBTs of the VSI in DVR. DVR
injects an equal positive voltage component in all three
phases which are in phase with the supply voltage to cor-
rect it. During sag event, the DVR-injected voltage is
added with load voltage. During swell event, the DVR-
injected voltage opposes load voltage. Thus, the load
voltage magnitude is sustained constant.
Figure 10 shows the transient performance of the system
subjected to sag event. During 0.2–0.4 s sag voltage is
created. The DVR injects voltage which is added with sag
voltage. The DVR-injected voltage for R-phase is shown in
diagram which is in phase with sag voltage. Thus, the load
voltage is regulated to constant amplitude.
The swell voltage is created at 0.2 s. The DVR-injected
voltage and compensated output voltage are given in
Fig. 11. The proposed DVR is also tested for unsymmet-
rical sag. The simulation results obtained are shown in
Fig. 12. For unsymmetrical sag event, the unbalanced
three-phase sag voltages are converted to a balanced pos-
itive sequence dc voltage component which is used to
generate reference voltages for VSI and the negative
sequence component which is completely eliminated using
SRF control algorithm. The output results obtained for
unsymmetrical sag are shown in Fig. 11a–c.
The magnitude of injected DVR voltage with different
angles of injection (0�, 30�, 45�, 60�, 90�) with respect to
supply voltage is observed. The DVR-injected voltage,
phase current and the KVA ratings of the DVR for the four
injection schemes are given in Table 2. The angle of 0�represents that in-phase injected voltage. The angle 90�shows that the injected voltage is in quadrature with line
Fig. 9 Proposed SRF–ANFIS controller block
R. Bhavani, N. Rathina Prabha: Simulation of Reduced Rating Dynamic Voltage Restorer using SRF–ANFIS Controller
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current. Among these five injection angles, it is inferred
that the amplitude of voltage injected by DVR for a certain
voltage sag using Scheme 1 is much less than that of
Scheme 5. The same analysis is carried out for voltage
swell. DVR should supply the required minimum reactive
VAR for the mitigation of voltage swell. This can also be
obtained with the injection angle of 0�.The performance of the proposed DVR with SRF–
ANFIS controller for the same kind of voltage sag and
swell is also compared with intelligent controllers fuzzy
logic and ANFIS controller in terms of output power from
DVR. The results obtained are shown in Table 3 for volt-
age sag and Table 4 for voltage swell problem.
The controller’s performance is shown as graph in
Fig. 13 for sag and Fig. 14 for swell. From the results it is
observed that the rating of DVR is minimized using SRF–
ANFIS controller. Thus, the proposed DVR provides much
better compensation for PQ problems and also gives a very
good economical solution for PQ problems.
6 Experimental Verification of DVR
To experimentally verify the feasibility of the proposed
DVR, real time sag and swell issues were generated in our
test environment. Experiment is carried out in a 415 V AC
source connected to linear load. Voltage sag is generated
Fig. 10 Simulation output for sag a sag created, b DVR-injected voltage, c DVR-injected voltage (phase R), d compensated output voltage
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by adding 440 V, 10 A inductive loads. Voltage swell is
generated by adding 414 V, 7.5 A capacitive load. The
obtained PQ events recorded using PQ analyzer are shown
in Fig. 15a, b. Figure 15a shows recorded voltage sag
event for each phase. During sag, voltage magnitude in
each phase is reduced to 190 V with the duration of
18–28 s. Figure 15b shows recorded voltage swell event
for each phase. During swell, voltage magnitude in each
phase is increased to 360 V with the duration of 15–43 s.
The performance of the proposed DVR is tested by
simulating same kind of events using simulation. The DVR
successfully compensates the issues by injecting the
required voltage with minimum magnitude.
Fig. 11 Simulation output for swell a swell created, b DVR-injected voltage, c DVR-injected voltage (phase R), d compensated output voltage
R. Bhavani, N. Rathina Prabha: Simulation of Reduced Rating Dynamic Voltage Restorer using SRF–ANFIS Controller
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Fig. 12 Simulation output for a unsymmetrical sag, b DVR-injected voltage, c compensated output voltage
Table 2 Proposed DVR output
for voltage sagOutput parameters Voltage injection angle by DVR (in degrees)
Scheme 1 Scheme 2 Scheme 3 Scheme 4 Scheme 5
0 30 45 60 90
Voltage (V) 210 220 232 241 248
Current (A) 9 9 9 9 9
VA per phase 1890 1980 2088 2169 2232
Table 3 Performance
comparison: sagSchemes Angle of injection VA output from DVR
SRF–PI controller SRF–FUZZY controller SRF–ANFIS controller
1 0� 2208 2065 1890
2 30� 2351 2193 1980
3 45� 2512 2335 2088
4 60� 2713 2510 2169
5 90� 3208 2835 2232
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7 Conclusion
The operation of DVR has been demonstrated with the
SRF–ANFIS controller using various voltage injection
schemes for both symmetrical and unsymmetrical voltage
sag and swell problems. The performance comparison of
DVR with different schemes has been performed for the
design of Reduced Rating VSC for DVR. The reference
DVR voltage has been estimated using Synchronous Ref-
erence Frame (SRF) theory which minimizes error of
voltage injection. The simulation result shows that the
proposed SRF–ANFIS controller renders a better response
than the one obtained using fuzzy and ANFIS controllers.
A comparison of the performance of proposed Reduced
Rating DVR with different voltage injection schemes has
been done. Based on the performance analysis, it is con-
cluded that the voltage injection/absorption from DVR is
in-phase with the sag/swell voltage which reduces the
power injected/absorbed by DVR. This outcome in mini-
mum rating of DVR makes the DVR more economical with
compact size and is capable of providing very efficient,
deep compensation for all kinds of power quality problems
with minimum cost. It can be used at any places where the
PQ problem arises. It can also act as an economic alter-
native to UPS for applications involving larger distribution
lines. This work can also be extended to other PQ
problems.
Table 4 Performance
comparison: swellSchemes Angle of injection VAR output from DVR
SRF–PI controller SRF–FUZZY controller SRF–ANFIS controller
1 0� 2585 1659 1302
2 30� 2947 1961 1559
3 45� 3316 2255 1808
4 60� 3751 2591 2092
5 90� 4757 3157 2573
Fig. 13 Controller performance: sag
Fig. 14 Controller performance: swell
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Fig. 15 Recorded PQ events a sag, b swell
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Mrs. R. Bhavani graduated in
Electrical and Electronics
Engineering from Thiagarajar
College of Engineering, Madu-
rai, Tamil Nadu, India, in 2000.
In 2005, she received Master of
Engineering (M.E) degree in
Power Systems Engineering
from the same college. She had
3 years of teaching experience
in PSNA College of Engineer-
ing and Technology, Dindigul.
From 2009 to 2015, she was an
assistant professor in the
Department of Electrical Engi-
neering at Mepco Schlenk Engineering, Sivakasi, Tamil Nadu, India.
Since 2016, she has been as an Assistant Professor (Sr. grade) in the
same college. Now, she is pursuing her research in the field of power
quality (PQ) under Anna University, Chennai, India. Her research
activities are focused on analysis of PQ problems and applications of
custom power devices for PQ enhancement using artificial intelli-
gence techniques.
Dr. N. Rathina Prabha gradu-
ated in Electrical and Electron-
ics Engineering from
Thiagarajar College of Engi-
neering, Madurai, Tamil Nadu,
India, in 1989. In 2000, she
received Master of Engineering
(M.E) degree in Power Systems
Engineering from the same col-
lege. She received Ph.D. degree
in Electrical Engineering from
Anna University, Chennai,
Tamil Nadu, India, in 2011. She
had 12 years of teaching expe-
rience in PSNA College of
Engineering and Technology and Raja College of Engineering and
Technology. Since 2004, she was in the Department of Electrical
Engineering at Mepco Schlenk Engineering, Sivakasi, Tamil Nadu,
India. Currently, she is working as an Associate Professor in the same
college. Her research activities are focused on power quality assess-
ment and enhancement, modeling simulation of custom power devi-
ces and power systems.
R. Bhavani, N. Rathina Prabha: Simulation of Reduced Rating Dynamic Voltage Restorer using SRF–ANFIS Controller
123
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