Implementation of Fuzzy Logic Controlled Dynamic Voltage ...ijaema.com/gallery/332-september-2463.pdf · Implementation of Fuzzy Logic Controlled Dynamic Voltage Restorer for Power

Implementation of Fuzzy Logic Controlled Dynamic

Voltage Restorer for Power Quality Improvement

Deepali J. Aher

1, Prof. Jeevan J. Inamdar

2

1 PG Student, Department of Electrical, TSSM Bhivarabai Sawant College of Engineering & Research,

Narhe Pune University, India 2 Assistant Professor, Department of Electrical, TSSM Bhivarabai Sawant College of Engineering & Research,

Narhe Pune University, India

[email protected],

[email protected]

Abstract— One of the major concerns in the industry is the quality of the power in mainline. This has become more

important with the inclusion of new advanced power quality devices which are sensitive for small changes in the quality.

Power quality problem is an occurrence manifested as a nonstandard voltage, current or frequency that results in a failure

of end-use equipment’s. Sensitive industrial loads and utility distribution networks suffer from various types of outages

and service interruptions which may result in a significant financial 1oss. The Dynamic Voltage Restorer has become

popular as a cost-effective solution for the protection of sensitive loads from disturbances. In this paper comparative

performance of DVR analysed by using PI control technique, and Fuzzy logic control technique in MATLAB/SIMULINK.

This scheme can have controlled both linear and nonlinear loads efficiently. The major problems dealt here are the

voltages sag and voltages unbalances. The role of DVR to compensate load voltage is investigated during the different

supply conditions like voltage sag and supply voltage unbalance. In contrast to the conventional DVRs control methods,

the proposed Fuzzy logic control method gives better compensation of voltage sag to maintain load voltage. The

performance of the experimental set up is compared with that of MATLAB simulations of the fuzzy controlled DVR and

the results are verified.

Keywords— Dynamic voltage Restorer, PI and Fuzzy logic control technique, voltage sag/swell, THD.

I. INTRODUCTION

The hi-tech advancements have proven a path to the modern industries to extract and develop the innovative ideas within the

limits of their industries for the accomplishment of their industrial goals. The ultimate objective is to optimize the production

while minimizing the production cost and thereby achieving maximized profits while ensuring continuous production throughout

the period. As such a stable supply of uninterruptible power must be guaranteed during the production process. The cause of

demanding high-power quality is basically the modern manufacturing, and process devices, which operates at high efficiency,

requires a high-quality deficiency-free power supply for the fruitful operation of their machines. More exactly most of these

machine elements are designed to be very sensitive for the power supply variations. Adjustable speed drives, automation devices,

power electronic components are examples of such equipment’s. Failure to provide the required quality power output may

sometimes cause a complete shutdown of the industries which will make a major financial loss to the industry concerned. Thus,

the industries always demand high-quality power from the supplier or the utility. Among those power quality irregularity voltage

sags and swells or simply the fluctuating voltage situations are one of the most frequenting types of abnormality. As per IEEE

standard 1159-1995, the voltage sag is defined as the reduction of RMS voltage to a value between 0.1 and 0.9 p. u and lasting for

a duration between 0.5 cycles to 1 minute when the RMS voltage of voltage increases between the range of 0.1 to 0.9 p.u of the

nominal voltage for the time duration of 0.5 cycle to 1minute then the voltage is defined as voltage swell. As the power quality

problems are originated from the utility and customer side, the solutions should come from both sides. They are named as utility-

based solutions and customer-based solutions respectively.

The International journal of analytical and experimental modal analysis

Volume XI, Issue IX, September/2019

ISSN NO: 0886-9367

Page No:2905

The best solutions for both two types are FACTS devices (Flexible AC Transmission Systems) and Custom power devices.

FACTS devices are those controlled by the utility, whereas, the Custom power devices are operated, maintained and controlled by

the customer itself and installed at the customer end side. Both the FACT'S devices and Custom power devices are based on solid-

state power electronic components. Uninterruptible Power Supplies (UPS), Dynamic Voltage Restorers (DVR) and Active Power

Filters (APF) are examples of commonly used custom power devices. Among those APF is used to mitigate harmonic problems

occurring due to non-linear loading conditions, whereas UPS and DVR compensate voltage sag, voltage swell, and voltage

unbalance conditions. Dynamic voltage restorer can be used effectively for mitigation of voltage sag and swell. This paper gives

the operating principle, power circuit topologies. mathematical modelling, control philosophies and application of DVR for power

quality improvement [1]. Four different system topologies for dynamic voltage restorers (DVR’S) are analysed and tested, with a

focus on the methods used to acquire the necessary energy during voltage sag. The result is no storage topology with a passive

shunt converter on the load side first, followed by the stored energy topology with a constant dc-link voltage [2]. Power sagging,

and power swellings are the two main issues which are most prevalent in industries. Custom power devices are one of the

solutions to this problem. Dynamic voltage restorer (DVR) is one of such custom power devices which is used most frequently in

the power distribution. This paper is an attempt to present a review work on the DVR technology along with various applications

proposed in the past [3]. This paper represents direct and indirect control strategies of Dynamic Voltage Restorer (DVR). With the

development of information and automation techniques, dynamic voltage problems are once again in spotlight Simulation results

were presented to illustrate and understand the performances of DVR with direct control, and with an indirect control strategy.

The reliability and robustness of these control schemes in the system response to the voltage disturbances due to system fault or

load variations are proved in the simulation results [4]. The advantages and disadvantages of each possible configuration and

control techniques related to DVR are presented. Various compensations strategies and controllers have been presented in this

paper, aiming at the fast response, accurate compensation, and low costs. It helps to select the optimum control strategy and power

circuit configuration for DVR applications [5]. The performance of the device under various voltages sag type is described, where

the voltage sag types are introduced using various types of short circuit faults [6]. Typical standard information tracking or

detection of voltage sag methods such as the Fourier transform or phase-locked loop (PLL) are too slow in returning this

information when either applied to the injection voltage vector, or to the supply voltage directly, a new matrix method which can

compute the phase shift and voltage reduction of the supply voltage much quicker than Fourier transform or PLL [7]. A novel

approach to derive the compensation reference signal is presented in which the mitigation factors associated with the disturbances

are adjusted by the fuzzy scheme. Further a single step prediction horizon technique is implemented to regulate the switching

commands of the DVR [8]. An overview of the DVR, its functions, configurations, components, compensating strategies and

control methods are reviewed along with the device capabilities and limitations represent in this paper [9]. The proposed system

explained here is a polymer electrolyte membrane (PEM) fuel cell-based DVR. The energy from the fuel cell is stored in

the supercapacitor to restitute the voltage. In this paper proposed DVR, Z-source inverter is used instead of conventional inverter

because of buck-boost and shoot through capability. The simulation is performed using the three controller topologies: PI

controller, synchronous reference frame controller, and fuzzy controller and the results are verified using MATLAB-Simulink

environment. The planned system during this paper is that the Fuzzy logic technique that is compared with the PI control

technique. exploitation each management technique the DVR is controlled at the distribution aspect. This paper presents the

comparative study of Simulation ends up in MATLAB package. The experimental discovered of Fuzzy logic-controlled DVR.

Dynamic Voltage refinisher offers the results that show the higher performance of DVR over the traditional control techniques.

symbolic logic was 1st planned by Lotfi A. Zadeh of the University of California at Berkeley during a 1965 paper. He detailed on

his ideas during a 1973 paper that introduced the construct of "linguistic variables, that during this paper equates to a variable

outlined as a fuzzy set. Fuzzy logic is wide employed in machine management. The term "fuzzy" refers to the very fact that the

logic concerned will affect ideas that can't be expressed because the "true" or "false" however rather as "partially true". the

primary DVR system in North America was put in in 1996 - a 12.47 kV system settled in Anderson, South Carolina.Fig.1

describes the basic structure of DVR.



ISSN NO: 0886-9367

Page No:2906

Fig.1 Basic strucure of DVR

II. MATHEMATICAL MODELING OF DVR

The system impedance Zth depends on the fault level of the load bus. When the system voltage (Vth) drops, the DVR injects a

series voltage VDVR through the injection transformer so that the desired load voltage magnitude VL can be maintained.Fig.2

represents the equivalent circuit diagram of DVR.

Fig.2 Equivalent circuit diagram of DVR

The series injected voltage of the DVR can be written as,

DVR L + th L - thV = V Z I V (1)

Where,

Vth=equivalent Thevenin voltage of the system

VL= load voltage

Zth=equivalent Thevenin impedance of the system

IL= Load current and

*L L

L

L

P + QI =

V

(2)

When VL is considered as a reference equation can be rewritten as,

0DVR L th L th

V = V 0 Z I V (3)



ISSN NO: 0886-9367

Page No:2907

Where, α = angle of VDVR

β = angle of system impedance Zth

δ = angle of system impedance Vth

Ф = Load pf angle and

1

L

= tanP

(4)

The complex power injected by DVR is,

*

DVR DVR LS = V I (5)

III. CONTROL ALGORITHM

Increasing demands for flexibility and quick reactions in modern process operation and production methods result in nonlinear

system behaviour of partly unknown systems, and this necessitates the application of alternative control methods to meet the

demands. The fuzzy Logic theory represents the human idea to reach a solution. Basically, it combines artificial intelligence,

multivalued logic, and probability. Fuzzy logic controller behaves like human brain so it’s better with no need to tune the PID

controller. fuzzy system updates its parameters on each control algorithm. Fuzzy logic is one of the most popular technologies

now days. prominently used in all branches of technology, from medical sciences to the automotive controls.Fig.3 explains the

Fuzzy Logic technique. When the mathematical calculations are difficult then the Fuzzy logic controller is an excellent

alternative.

Fig.3 Fuzzy Logic

Fuzzy Logic Control Toolbox:

1. Provide an easy to use interface for applying a modern fuzzy logic technique.

2. Perform Fuzzy Logic control and identification.

3. Easily integrated into a model-based design using Simulink blocks.

4. Provide the ability to generate codes for various cases.

5. Supply a Fuzzy Inference engine that can execute your Fuzzy system as a standalone application.

The block diagram of proposed control scheme is as shown in Fig.4.

Fig.4 Proposed control scheme



ISSN NO: 0886-9367

Page No:2908

A. Fuzzification

The purpose of Fuzzification is to convert crisp input signal, change in error (∂e), output signal and error (e) into fuzzified

signals and these signals can be recognized by level of memberships in fuzzy sets. In Simulation study, the error and error rate are

defined by linguistic variables such as negative medium (NM), negative big (NB), negative small (NS), very small (VS), positive

big (PB), positive small (PS), and Recommended font sizes are shown in Table 1. positive medium (PM) characterized by

triangular membership function. The output is also defined by seven linguistic variables negative medium (NM), negative big

(NB), negative small (NS), very small (VS), positive big (PB), positive small (PS), and positive medium (PM) characterized by

membership function as shown in fig. Fig.5

Fig.(A) Input Variable Error

Fig.(B) Input variable Error rate

Fig.(C) Output variable output1

Fig. 5 Membership Functions for Input and Output

B. Decision Making

Input Variable Error Fig.(B) Input variable Error rate Fig.(C) Output variable output1 Fig.5 describes the Membership

Functions for Input and Output. Decision making in the fuzzy process is realized by the Mamdani method. Mamdani inference

method is used because it can easily obtain a relation between input and output. The If then principles characterize a Fuzzy

Inference System (FIS) by associating the output to the input. The set of rules for the Fuzzy controller is as shown in Table I.

There are 49 rules for a fuzzy controller. The output membership function for each rule is given by Min (minimum)operator. Max

operator is used to getting the combined fuzzy output from the set of outputs of Min operator. The output is produced by fuzzy

sets and fuzzy logic operations by evaluating all the rules.

TABLE I

RULE BASE REPRESENTATION



ISSN NO: 0886-9367

Page No:2909

C. Defuzzification

The output of fuzzy logic controller comes in the form of linguistic labels and the crisp solution variable obtained from the

linguistic labels. After this conversion control of system is possible. This is accomplished by utilizing a defuzzifier.

IV. SIMULATION SCHEMES

A. Simulation schemes with PI Controller

Fig.6 shows the simulation diagram of PI controller. Table II represents the specifications of system parameters used in

MATLAB software.

TABLE II

SIMULATION PARAMETERS USED IN PI DVR SIMULATION

Parameters Specifications

Supply voltage 415V,3 phase,50Hz

Injection transformer 1:1

Inverter IGBT,3 arms,6 pulse

DC link voltage 700V

PI Controller Kp =0.3, Ki =1



ISSN NO: 0886-9367

Page No:2910

A Proportional integral (PI) controller controlled by sum of the error which is difference between the actual sensed output and

desired set point and integral of that value. The controller output when compared at pulse width modulation signal generator

results in the desired firing sequence. The modulated angle is applied to the PWM generator. Sinusoidal signal control voltage is

phase modulated by using the angle ∂ and the modulated three phase voltages are given as,

A

B

C

V = 1 *Sin ( t + ) (6)

V = 1 * Sin ( t + + 2 /3) (7)

V = 1* Sin ( t + + 4 /3) (8)

Fig.6 Simulink Scheme of DVR with PI controller

B. Simulation schemes with Fuzzy Logic Controller

The simulation of dynamic voltage restorer with Fuzzy logic controller is done using MATLAB/SIMULINK and Simpower

systems software. The specifications of system are as shown in Table III.



ISSN NO: 0886-9367

Page No:2911

TABLE III

SIMULATION PARAMETERS USED IN FUZZY DVR SIMULATION

Parameters Specifications

3 phase voltage Source 415V,3ph,50Hz

DC Bus voltage 700V

Injection transformation ratio 1:1

Line frequency 50Hz

RL Load Active power 10 KW

Reactive

power

100 KVAR

PWM switching frequency 1048 Hz

The 3 phase programmable voltage source that compared with 3-phse reference voltage source and error along derivative of error

is multiplexed and given to fuzzy logic controller block is compared with repeating sequence to generate the gating pulses for

IGBT as shown in Fig7b).The control block has a three phase programmable voltage source incorporated to inject voltage

harmonics and to apply voltage sag/swell Conditions in source voltage.

Fig.7a) Overall Simulink Scheme of FLDVR



ISSN NO: 0886-9367

Page No:2912

Fig.7 b) Simulink Scheme of Fuzzy controlled DVR

V. SIMULATION RESULTS

A. Simulation Results of PI DVR

A balanced voltage sag in the source voltage of 40% is introduced in the system from 0.2 to 0.4 sec in supply voltage. Fig.8

shows the Source voltage, injected voltage, load voltage.

(a)

(b)

(c)



ISSN NO: 0886-9367

Page No:2913

Fig.8 Performance under balanced voltage sag (a)Three phase source voltage during balanced voltage sag (b) Injected voltage by PIDVR (c) Three phase source

voltage

B. Simulation Results of Fuzzy DVR

A balanced voltage sag in the source voltage of 40% is introduced in the system from 0.2 to 0.4sec supply voltage. Fig.9 shows

the Source voltage, injected voltage, load voltage and load THD as,

(a)

(b)

(c)

(d)

Fig.9 Performance under balanced voltage sag (a)Three phase source voltage during balanced voltage sag (b) Injected voltage by FLDVR (c) Three phase load

voltage (d)source voltage THD.

TABLE IV

SIMULATION RESULT TABLE

Control Technique Voltage Sag in System Voltage Restored in

System

%THD

PI Controller 40% Up to 60% 4.42%

Fuzzy Controller 40% Up to 98% 2.83%

VI. EXPERIMENTAL SET UP



ISSN NO: 0886-9367

Page No:2914

A. Block Diagram of Hardware Model

The block diagram of the complete hardware model as shown in Fig.10. The Hardware arrangement of single-phase Dynamic

Voltage Restorer to mitigate the voltage sag and swell consist of single-phase Power supply, Driver & inverter circuit, control

circuit, Isolated power supply (Battery), Isolation transformer, load, and 9V transformer to supply Zero crossing detector .

Fig.10 Block Diagram of Hardware Model

Table V represents the components and its rating used in hardware model

TABLE V

HARDWARE COMPONANTS AND ITS RATINGS

Hardware Component Rating

Step Down Transformer 230/12V,1A

Isolation transformer 230/9V

Battery (DC Supply) 12V,1.3Ah

Microcontroller PIC16F877A

Load R=1.5, L=1mH

Fig.11 shows the experimental set up of single-phase DVR. Under normal operating condition, the load voltage is 12V. When a

nonlinear load is connected through a switch, then harmonics are generated so that load voltage is continuously changing. As the

load voltage changes continuously, load voltage and reference voltage are sensed by the sensor and these difference in voltage is

given to the controller. The controller compares voltage and finds the difference between them which gives a command to the

PWM circuit to generate a required voltage. External DC source is used to inject required voltage through an inverter. The inverter

gives dropped voltage to the load through injection transformer, so that load voltage is maintained constant.



ISSN NO: 0886-9367

Page No:2915

Fig.11 Experimental set up of Single phase FLDVR

VII. HARDWARE RESULTS

Under the normal operating condition, the source voltage is 11.4V. Fig.12 shows the voltage waveform during normal operating

condition.

Fig.12 Voltage waveform during normal operation

When nonlinear load is connected through switch harmonics are generated,50% sag is created in system as shown in Fig.13.

Fig.13 Voltage waveform during sag condition



ISSN NO: 0886-9367

Page No:2916

As the load voltage changes continuously, load voltage and reference voltage are sensed by sensor and these difference in voltage

is given to the controller. Controller compares voltage and finds the difference between them which gives command to the PWM

circuit to generate required voltage. External DC source is used to inject required voltage through inverter. Inverter gives dropped

voltage to the load through injection transformer, so that load voltage is maintain constant.

Fig.14 Voltage waveform with operation of DVR

Fig.14 shows the voltage waveform with DVR operated, and load voltage gets maintained.

Fig.15 Load Voltage (rms) waveform

As shown in Fig.15 During normal condition the supply voltage is 12V .50% sag is created in the system as shown in above fig.

This is detected by Fuzzy controller technique as we use in this project. The controller send signal to inverter circuit trigger the

gate pulses to generate the missing voltage. This voltage is injected through the injection transformer and voltage is restored at the

load side. Almost 98% voltage is restored using this proposed system.



ISSN NO: 0886-9367

Page No:2917

Fig.16 Source voltage THD (1.8%) using Fuzzy Control technique

VIII. CONCLUSION

The voltage sag/swell is compensated very effectively in DVR which is very simple to implement. This paper comprises a

Simulink model for PI and Fuzzy logic controllers. The facility available in MATLAB/SIMULINK is used to carry out an

extensive simulation study. The simulation results clearly show that when 40% sag introduced in the system then PI controlled

DVR restored voltage up to 60% at the load side, while Fuzzy logic-controlled DVR restored voltage up to 98% at the load side. It

proves that the Fuzzy logic controller gives better performance. In this paper Hardware Implementation of Fuzzy controlled DVR

has been presented. The hardware results also show clearly that the DVR is very fast responsive and less expensive device than

other custom power devices. It reduces the THD 1.8% as compared to the conventional techniques. The proposed technique

achieved better performance in Voltage Sag compensation as compared to conventional techniques. It accomplished the objective

of the project to protect the sensitive load from the voltage sag in the distribution system.

REFERENCES

[1] John Godsk Nielsen and Frede Blaabjerg, Fellow IEEE ―A Detailed Comparison of System Topologies for Dynamic Voltage Restorers‖ IEEE

TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 41, NO. 5, SEPTEMBER/OCTOBER 2005.

[2] Jovica. V. Milanovic, Fellow, IEEE, and Yan Zhang, Student Member, IEEE ―Global Minimization of Financial Losses Due to Voltage Sags with

FACTS Based Devices‖ IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 25, NO. 1, JANUARY 2010.

[3] Yan Zhang and Jovica V. Milanovic´, Fellow, IEEE ―Global Voltage Sag Mitigation With FACTS-Based Devices‖ IEEE TRANSACTIONS ON

POWER DELIVERY, VOL. 25, NO. 4, OCTOBER 2010

[4] Majid Moradlou, Student Member, IEEE, and Hamid R. Karshenas, Member, IEEE ―Design Strategy for Optimum Rating Selection of Interline DVR‖

IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 26, NO. 1, JANUARY 2011

[5] Anish Prasai, Student Member, IEEE, and Deepak M. Divan, Fellow, IEEE ―Zero-Energy Sag Correctors—Optimizing Dynamic Voltage Restorers for

Industrial Applications‖ IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 44, NO. 6, NOVEMBER/DECEMBER 2008

[6] F. Mohammad Mahdianpoor, Rahmat Allah Hooshmand, Member, IEEE, and Mohammad Ataei ―A New Approach to Multifunctional Dynamic

Voltage Restorer Implementation for Emergency Control in Distribution Systems‖ IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 26, NO.

2, APRIL 2011

[7] Zhikang Shuai, Member, IEEE, Peng Yao, Z. J. Shen, Fellow, IEEE, Chunming Tu, Member, IEEE, Fei Jiang, and Ying Cheng ―Design Considerations

of a Fault Current Limiting Dynamic Voltage Restorer (FCL-DVR)‖ IEEE TRANSACTIONS ON SMART GRID2014.

[8] Deepak Somayajula, Member, IEEE, and Mariesa L. Crow, Fellow, IEEE ―An Integrated Dynamic Voltage Restorer-Ultracapacitor Design for

Improving Power Quality of the Distribution Grid‖ IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 6, NO. 2, APRIL 2015

[9] P. Jayaprakash, Member, IEEE, Bhim Singh, Fellow, IEEE, D. P. Kothari, Fellow, IEEE, Ambrish Chandra, Senior Member IEEE, and Kamal-Al-

Haddad ―Control of Reduced Rating Dynamic Voltage Restorer with Battery Energy Storage System‖ IEEE TRANSACTION 2013.

[10] Abdul Mannan Rauf and Vinod Khadkikar, Member, IEEE ―An Enhanced Voltage Sag Compensation Scheme for Dynamic Voltage Restorer‖ IEEE

TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2014.

[11] Changjiang Zhan, Vigna Kumaran Ramachandaramurthy, Atputharajah Arulampalam, Chris Fitzer, Stylianos Kromlidis, Mike Barnes, and Nicholas

Jenkins ―Dynamic Voltage Restorer Based on Voltage-Space-Vector PWM Control‖ IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS,

VOL. 37, NO. 6, NOVEMBER/DECEMBER 2001.

[12] Chandan Kumar, Student Member, IEEE, and Mahesh K. Mishra, Senior Member, IEEE ―Predictive Voltage Control of Transformer less Dynamic

Voltage Restorer‖ IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2014



ISSN NO: 0886-9367

Page No:2918

[13] A. Khoshkbar Sadigh, K.M. Smedley ELSEVIER ―Fast and precise voltage sag detection method for dynamic voltage restorer (DVR) application‖

Electric Power Systems Research 130 (2016) 192–207.

[14] Debasis Patel, Arup Kumar Goswami, Santosh Kumar Singh ELSEVIER ―Voltage sag mitigation in an Indian distribution system using dynamic

voltage restorer‖ Electrical Power and Energy Systems 71 (2015) 231–241

[15] Hyosung Kim, Member, IEEE, and Seung-Ki Sul, Senior Member, IEEE ―Compensation Voltage Control in Dynamic Voltage Restorers by Use of

Feed Forward and State Feedback Scheme‖.



ISSN NO: 0886-9367

Page No:2919

Documents

Implementation of Fuzzy Logic Controlled Dynamic Voltage ...ijaema.com/gallery/332-september-2463.pdf · Implementation of Fuzzy Logic Controlled Dynamic Voltage Restorer for Power