Mitigation of Voltage Quality Problem Using Dynamic Voltage Restrorer (DVR)
(Comparison Between ANFIS, PI and FIS)
*Ramadan Salem Ramadan Ali, Muhammad Nizam
Mechanical Engineering Posgraduate program,
Sebelas Maret University, Surkarta.
Email:aspirpeace@gmail.com, ramadansalem2014@gmail.com
Abstarct: The objectives of this research are1) to examine the effect of power quality on distribution
network 2) to evaluate the performance of DVR control by using ANFIS, PI and FIS method in order
to determine the value of power injection into the system when recovery of voltage has taken place.
Simulation model for DVR was proposed, ANFIS and FIS Based Controller design and DVR with PI
controller were employed. The result of the reduction of sag with the application of the three methods,
the PI Controller, FIS and ANFIS on the same electrical network indicated that ANFIS functions in
reducing voltage sag. The simulation using FIS and ANIFIS controller with DVR, giving 0.4 Ω fault
resistance during period 500 to 900 ms shows the voltage sag at load point 96.5% and 97%. This
shows that the three phase ANFIS is best for FIS and PI control.
Key Words: ANFIS, DVR,FIS, Fuzzifier, PI, Sag
Introduction
A power quality problem is an occurrence manifested as a nonstandard voltage, current or
frequency that results in a failure or a mis-operation of end use equipment. Jang (1993) mentioned
that ANFIS train can have a potentiality to better generalization capability and the advances of neural
network techniques in control can promote those of ANFIS as well, and vice versa. Similarly, a neurofuzzy system uses learning methods derived from artificial network in order to find the parameters of
fuzzy system which includes appropriate membership functions and fuzzy rules. And one of the
neuro-fuzzy systems in which learning algorithm is coincided integrates learning approaches ANFIS
system (Li et al., 2009; Sun et al., 2005 and Hasiloglu et al., 2004).
ANFIS can be used for identifying extremely nonlinear system parameters with high
accuracy; extremely fast parallel computation and fault tolerance characteristics (Kamel and Hassan,
2009; Rashidi, 2004).According to Kodad et al. (2010) the main advantages of the ANFIS are that it
works well with linear techniques, optimization & adaptive techniques. Additionally, the tedious task
of training of membership functions is done in ANFIS because the speed of operation of ANFIS is
much faster than the other control strategies. Likewise, Rashidi (2004) presented that Fuzzy logic
combined with neural networks is one of the successful applications in the control engineering field
which can be used to control various parameters of the real time systems. In another report Aware et
al. (2000) showed that Since ANFIS design starts with a pre-structured system, degree of freedom
(DOF) for learning is limited.
The Modeling and Simulation of Dynamic Voltage Restorer (DVR) using ANFIS is one of
the promising applications of ANFIS. As Sudha et al. (2012) reported the performance of the
simulated DVR with ANFIS controller is efficient than conventional PI controller to compensate the
voltage sag on test system considered for study with the application of three-phase to ground fault for
100 ms. In case of dynamic response of ANFIS denoted by Bawane et al. (2005) most notably ANFIS
based on control can be easily combined with the ANFIS based fault identifier to form integrated
system, which can improve dynamic response of the HVDC system. Roy (2005) noted that the
adaptation of both triangular and bell- shaped membership functions in ANFIS achieved very
satisfactory accuracy of 96.13% and 97.84%, respectively. Triangular membership functions are
chosen as the best membership function for ANFIS training of the experimental data (Salmalian and
Soleimani, 2011). An adaptive neural network is a network structure consisting of a number of
nodes connected through directional links. Each node is characterized by a node function with
fixed adjustable parameters (Alavander and Grabowski et al., 2000; Nigam, 2008; Nasir Uddin & Hao
wen, 2005, Roy, 2005).
The three main voltage controllers, which have been proposed in literature, are Feed-forward
(open loop), Feedback (closed loop) and Multi-loop controller (Margo et al., 2008). The Feed-forward
voltage controller is the primary choice for the DVR, because of its simplicity and fastness. Each
controller is composed by a PI controller and selective controller. The PI controller tracks the
reference signal at the positive sequence of the fundamental frequency and the selective controller its
negative sequence. In that way, the DVR can compensate balanced and unbalanced voltage sags
(Pakharia and Gupta, 2012). (Bhaskar et al., 2010) presented the performance of a DVR in mitigating
voltage sags and swells. (Devaraju et al.,2010). They implemented a PWM-based control scheme for
D-STATCOM and DVR. Omar and Rahim (2009) also presented the low voltage dynamic.
Research Methodology
Fig 1 Simulink model for DVR use PI
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Fig .2 Simulink model for DVR use FIS
Firstly, PI control was used as control to investigate performance of DVR. Next, the proposed
method was implemented by changing PI control Adaptive Neuro Fuzzy Injection system (ANFIS) in
order to investigate the voltage size and duration of injection during faulty. The control system of a
DVR plays is an important role, with the requirements of fast response in the face of voltage sags and
variations in the connected load.
The main purpose of the control system is to maintain a constant voltage magnitude at the
point where a sensitive load is connected, under system disturbances. It will also look after the D.C.
link voltage using the DC-charging unit (Ibrahim et al., 2010).
Result and Discussion
The first simulation was done without DVR and a three phase fault was applied to the system
for time duration of 400 ms. The second simulation is carried out at the same scenario as above but
using DVR with PI controller. The third simulation is carried out at the same scenario as above but
using DVR with ANFIS.
PI Controller
A DVR is connected to the system through a series transformer with a capability to insert a maximum
voltage of 70% of the phase to ground system voltage. Simulation condition : The network is
composed by a 13 kV, 50 Hz generation system, represented by in Fig.1 feeding two transmission
lines through an 3-winding transformer connected in Y/Δ/Δ13/115/115 kV. Transmission lines feed
two distribution network through two transformers connected in Δ/ Y, 115/11KV.
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Fig .3 PI Controllers with DVR and without DVR
The first simulation using PI controller without DVR gives 0.4 Ω fault resistance, during
period 400. The voltage magnitude is drop to 0.7 p.u. The second test has been done by simulation
using PI controller with DVR with same value of resistance, during period 400 ms.
The figure1. shows simulation voltage 0.67 p.u at the beginning is drop and goes to stable at PI
controller has effective enough to stable to restore voltage of 0.96 p.u.
ANFIS
The input-output data pairs for training the ANFIS were generated using the conventional PI
controller. FIS structure with Sugeno model containing 9 rules (Table.1) has 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 (N, Z, P) where N = negative, Z = zero, P =
positive, with triangular membership function. The output variable OUT given by ANFIS training is a
vector of constants. OUT = [OUT1, OUT2, OUT3, OUT4, OUT5, OUT6, OUT7, OUT8, OUT9],
Where OUT1 = -1397, OUT2 = -1397, OUT3 = -1397, OUT4 = -38.58, OUT5 = -38.58, OUT6 = 38.58, OUT7 = 1319, OUT8 = 1320, OUT9 = 1320.
Input error
Input ∆error
output
-1
-100000
-1397
-1
0
-1397
-1
100000
-1397
0
-100000
-38.58
0
0
-38.58
0
100000
-38.58
1
-100000
1319
1
0
1320
1
100000
1320
Table.1 Data training ANFIS
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Domain is the set of fuzzy overall values are allowed in the universe of
discourse and may be operated in a fuzzy set. Domain is the set of real numbers are constantly rising
monotonically from left to right. Domain values can be either positive or negative numbers.
Training Adaptive Neuro Fuzzy Inference Systems Using the ANFIS Editor GUI
To train a FIS, a stipulating training data of the input / output of data which is represented the
system modeled, after conducted by loading of data input / output later; then conducted of forming
According to FIS as the desired order. Then use the data in training the hybrid method by applying
error tolerance 0 and epoch as much as much 20. By conducting training use the hybrid is expected
this method can reach the stability with the value error as small as possible.
Fig .5 train data ANFIS
Fig.6 Test training data and FIS output
Fig.6 Shows the output FIS by that training result gives the maximal result so that output
classifications earn the check of at expected place.
Simulation with Fuzzy Toolbox Research on energy savings with the use of fuzzy logic control begins
with a simulation using the software.
To arrange to use fuzzy logic reasoning, Toolbox software Fuzzy is used. Simulation with Fuzzy
Toolbox software is intended for determining the pattern of decision-making rules of thumb that have
been given.
The basic rule of fuzzy logic gives the relationship between input and output, when arranged in a
matrix table it appears as shown in Table.5 The basic rule editor compiled using the basic rules that
are already available in MATLAB. Once organized into basic rule is then examined by the rule and
the rule surface viewer.
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Rule surface is used to see the pattern of decision-making rule base used.
Fig. 7. Rule Surface
Rule viewer is used for approximate reasoning result fuzzy logic. By entering the second input value
on the rule viewer can know the result of reasoning by fuzzy logic.
Table 3. Input-Output Relations fuzzy logic
At the time of Error = -40 (NB) which is the distance between the current speed of calculation the
value of a given reference speed and ∆Error (-10) because slowdown is large enough so that is
coupled with the fast PWM (DPWM = 13.1). Error occurs when small but large enough acceleration
is then arrested PWM (DPWM = 0) on the condition that at the time it reaches the reference speed
PWM avoid excess value.
ANFIS is a merger between the fuzzy neural networks. The difference between ANFIS and
FIS is ANFIS requires a learning process before entering into a truth classification grouping. While
the FIS mapping without the need for learning. So ANFIS is suitable for use with the case when the
data is very complex and difficult to apply in a simple calculation formula. ANFIS has the function to
reduce the voltage sag.
Fig.8. three phase fault scenario Result of comparison use the PI Controller, FIS and ANFIS.
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When given three-phase fault with the system connected to the DVR with a PI controller, the voltage
restore can be up to 0.93 p.u; and with FIS and ANFIS are 0.96, and 0.97 respectively. From three
cases above the ANFIS show the best solving control to restore the voltage camper FIS and PI
controller. In order to set stability PI control is better than FIS and ANFIS. The oscillation still occurs
at the beginning of the control ANFIS and FIS. From the three cases of voltage sag it can be said the
PI, FIS and ANFIS are very good for control the DVR in order to handle the voltage sag.
Conclusion
DVRs are effective custom power devices for voltage sags mitigation. They inject the
appropriate voltage component and correct rapidly any abnormality in the voltage and keep the load
voltage at reference and constant at the nominal value. In this study a reliable controller with high
performance for dynamic voltage restorer 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. From the three cases of voltage sag it can be
concluded the PI, FIS and ANFIS are very good to control the DVR in order to handle the voltage sag.
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