erp.mepcoeng.ac.in · minimizes the hazard of load tripping from very deep sag and swell problems....

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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

[email protected]

N. Rathina Prabha

[email protected]

1 Mepco Schlenk Engineering College, Sivakasi 626005, India

123

Int. J. Fuzzy Syst.

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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

International Journal of Fuzzy Systems

123


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

123


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


123


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


123


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


123


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


123


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


123


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


123


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


123


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


123


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Fig. 15 Recorded PQ events a sag, b swell


123


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.


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