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ISSN: 2278 – 909X International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE)

Volume 4, Issue 7, July 2015

2006

All Rights Reserved © 2015 IJARECE

A Novel Intelligent Controlling Technique for

Distributed power flow Controller for Power

Quality Improvement

D.Radhika1, Dr. B.V.Sanker Ram

2

Abstract – Non linear loads are the cause for most of the

power quality issues. Insufficient reactive power causes

Voltage Sag as well excess reactive power causes the Swell.

To control the voltage to a desired value, regulation of

reactive power is required. The existing UPFC approach for

the improvement of voltage profile is expensive. In this

paper a novel intelligent control scheme is proposed for the

new D-FACTS device, Distributed Power Flow Control for

the improvement of voltage profile of a IEEE Standard 4

bus system. Firstly conventional PI controller is used

inconjuction with DPFC, connected to a load bus. To

improve the efficiency of controlling action of DPFC, first a

fuzzy controller is proposed and later the results are

compared with Neuro controller. The addition of Neuro

controller with the DPFC improves the Voltage Profile in a

better way. The entire circuit is simulated using MATLAB.

Keywords: DPFC, PI Controller, Power Quality, Fuzzy

Logic, Neural Network, Voltage Sag, Voltage Swell.

I. INTRODUCTION

Electrical power is perhaps the most essential

raw material used by commerce and industry today. It is

required as a continuous flow. The most obvious power

defects are complete interruption (which may last from a

few seconds to several hours) and voltage dips or sags

where the voltage drops to a lower value for a short

duration. Naturally, long power interruptions are a

problem for all users, but many operations are very

sensitive to even very short interruptions. Voltage control

in an electrical power system is important for proper

operation for electrical power equipment to prevent

damage such as overheating of generators and motors, to

reduce transmission losses and to maintain the ability of

the system to withstand and prevent voltage collapse. In

general terms, decreasing reactive power cause voltage to

fall while increasing it cause voltage to rise [13]. While

the majority of voltage dips and interruptions originate in

the transmission and distribution system [12]. A voltage

collapse occurs when the system try to serve much more

load than the voltage can support [13]. Due to the use of

the different types of sensitive electronic equipments, PQ

issues have drawn substantial attention from both utilities

and users [3].

In this paper, a distributed power flow controller

is introduced to mitigate voltage waveform deviation and

improve power quality within seconds. The DPFC

structure is derived from the UPFC structure that is

included one shunt converter and several small

independent series converters as shown in Fig.1 [7].

Fig. 1: The DPFC Structure

A DPFC is connected to a 4 bus transmission system as

shown in Fig. 2. The DPFC consists of one shunt and

several series-connected converters. The shunt converter

is similar as a STATCOM, while the series converter

employs the D-FACTS concept, which is to use multiple

single-phase converters instead of one large rated

converter [1]. Within the DPFC, there is a common

connection between the ac terminals of the shunt and the

series converters, which is the transmission line.

Therefore, it is possible to exchange the active power

through the ac terminals of the converters.

DPFC consists of three types of controllers; they are

central controller, shunt control, and series control. The

central control generates the reference signals for both

the shunt and series converters of the DPFC[1].

Fig. 2 Simulated IEEE Four Bus System

All the reference signals generated by the central control

are at the fundamental frequency. Each series converter



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has its own series control. The controller is used to

maintain the capacitor dc voltage of its own converter

and to generate series voltage at the fundamental

frequency that is prescribed by the central control. The

shunt converter‟s fundamental frequency control aims to

inject a controllable reactive current to grid and to keep

the capacitor dc voltage at a constant level [1].

II. CONVENTIONAL CONTROL OF DPFC

This paper proposed a Conventional PI controller for the

improvement of the performance of the DPFC connected

to a 4 bus system.

Fig.3 Distributed Power Flow Controller with PI Contol

The performance with DPFC and without DPFC is

observed and it is found that with the conventional

controller the voltage of the bus is not reached to nominal

value after clearance of sag and swells.

Fig. 4 Voltage response of DPFC with PI and without PI

III. DPFC WITH FUZZY CONTROL

Modeling and control of dynamic systems belong to

the fields in which fuzzy set techniques have received

considerable attention, not only from the scientific

community but also from industry. Many systems are

not amenable to conventional modeling approaches

due to the lack of precise, formal knowledge about the

system, due to strongly nonlinear behavior, due to the

high degree of uncertainty, or due to the time varying

characteristics. To improve the efficiency of DPFC in

the system an intelligent control strategy, namely

fuzzy control mechanism is proposed for the power

system engineer. The controlled strategy implemented

by the engineers are prepared as set of rules that are

simple to carry out manually but difficult to

implement by using conventional control strategy.

In this proposed fuzzy controller approach

the inputs are error (e) and change in error (∆e)

generates required control signal. The design procedure

of FLC consists of the following modules: 1)

Fuzzification 2) Fuzzy Rule-base 3) Fuzzy Inference

Engine (Decision Making Logic) and 4) Defuzzification.

A Fuzzy controller operates by repeating a cycle of

following four steps. First, measurements are taken of all

variables that represent relevant conditions of the

controlled process (Universal Discourse). Next, these

measurements are converted into appropriate fuzzy sets

to express measurement uncertainties (Fuzzification).

Fuzzy models can be seen as logical models which use

"if-then" rules to establish qualitative relationships

among the variables in the model. Fuzzy sets serve as a

smooth interface between the qualitative variables

involved in the rules and the numerical data at the inputs

and outputs of the model. The rule-based nature of fuzzy

models allows the use of information expressed in the

form of natural language statements and consequently

makes the models transparent to interpretation and

analysis. At the computational level, fuzzy models can be

regarded as flexible mathematical structures that can

approximate a large class of complex nonlinear systems

to a desired degree of accuracy. The fuzzified

measurements are the used by the inference engine

(Decision Making Logic) to evaluate the control rules

stored in fuzzy rule base. The result of this evaluation is a

output fuzzy set (or several fuzzy sets) defined on the

universe of possible actions and the degree of

membership of the output fuzzy set can be calculated by

using Root Sum Square Method. This fuzzy set is then

converted, in the final step of the cycle, into a crisp

(single) value that, in some sense, is the best

representative of the fuzzy set (Defuzzification). The

defuzzified value represents the actions taken by the fuzzy

controller in individual control cycles. Now, in the

following Sections we develop the various components

of FLC to solve power quality problem. Fuzzy controller

is developed to generate required control signal it

receives the input signal from the system and processing

the data in different stages and generate required

output[15].

Fig. 5 Fuzzy Logic Controller



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III.I. Fuzzy Control Algorithm

The following algorithm is proposed for designing of

Fuzzy logic controller to mitigate power quality problem

of four bus system.

Step 1: Select the input variables to enhance power

quality issues like swell, sag. Error and change in

error are selected as fuzzy inputs and the output

of the FLC is proposed as the phase angle of

injected current(D-STATCOM) and phase angle of

injected voltage(D-SSSC).

Step 2: Selected input and out variables are then

partitioned in to 2 regions and their membership

functions have been defined.

Step 3: With the knowledge of the power quality issue

like voltage swell and voltage sag (through the

method, “Expert Experience and Control

Engineering Knowledge”), 4 rules are framed to

decide the membership function value of the

output variable. The rules are then tabulated in the

Decision-Table.

Step 4: Fuzzy output sets are formed and the strengths

of each of the output membership function is

estimated by Root Mean Square method.

Step 5: By applying Defuzzification, Crisp output is

obtained.

Step 6: With the crisp value, output signal is generated.

Fig. 6 Fuzzy Control logic of DPFC

IV. ANN CONTROL OF DPFC

Neural networks represent a class of very powerful,

general-purpose tools that have been successfully applied

to prediction, classification and clustering problems. The

popularity of neural networks is based on their

remarkable versatility, abilities to handle both binary and

continuous data, and to produce good results in complex

domains. When the output is continuous, the network can

address prediction problems, but when the output is

binary, the network works as a classifier.

An artificial neural network consists of a number of very

simple and highly interconnected processors, also called

neurons, which are analogous to the biological neurons in

the brain. The neurons are connected by weighted links

passing signals from one neuron to another. Each neuron

receives a number of input signals through its

connections; however, it never produces more than a

single output signal. The output signal is transmitted

through the neuron‟s outgoing connection[13].

Fig. 7 Architecture of Artificial Neural Network

A typical ANN is made up of a hierarchy of layers, and

the neurons in the networks are arranged along these

layers. The neurons connected to the external

environment form input and output layers. The weights

are modified to bring the network input/output behavior

into line with that of the environment. Increasing the

complexity of an ANN, and thus its computational

capacity, requires the addition of more hidden layers, and

more neurons per layer.

Each neuron is an elementary information-processing

unit. It has a means of computing its activation level

given the inputs and numerical weights. To build an

artificial neural network, we must decide first how many

neurons are to be used and how the neurons are to be

connected to form a network. In other words, we must

first choose the network architecture. Then we decide

which learning algorithm to use. And finally we train the

neural network, that is, we initialize the weights of the

network and update the weights from a set of training

examples[13].

The neuron computes the weighted sum of the input

signals and compares the result with a threshold value, θ.

If the net input is less than the threshold, the neuron

output is -1. But if the net input is greater than or equal to

the threshold, the neuron becomes activated and its

output attains a value +1.

The neuron uses the following transfer or activation

function[13]

Xk+1 = Xk – [JTJ + µI]

-1J

Te

Where J is the Jacobian matrix that contains first

derivatives of the network errors with respect to the

weights and biases and e is the vector of network errors. J

can be computed through back propagation technique.

This function uses the Jacobian for calculations, which

assumes that performance is a mean or sum of squared



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errors. Therefore, networks trained with this function

must use either the mse or sse performance function.

Levenburg marquardt algorithm can train any network as

long as its weight, net input, and transfer functions have

derivative functions.

Training stops when any of these conditions occurs:

The maximum number of epochs (repetitions)

is reached.

The maximum amount of time is exceeded.

Performance is minimized to the goal.

The performance gradient falls below min_grad.

mu exceeds mu_max.

Validation performance has increased more

than max_fail times since the last time it decreased

(when using validation).

Fig. 8 ANN Control of Shunt Compensator

Fig. 9 ANN Control of Series Compensator

V. RESULTS

In this paper a comparison is made on the performance of

DPFC with PI, Fuzzy and Neural Network Controlling

techniques for the improvement of Power Quality issues,

voltage sag and swell. The proposed controllers are

evaluated by computer simulation in

MATLAB/SIMULINK. The proposed intelligent

controllers improved the power quality of the

transmission system at load bus. The results are taken on

2% tolerance base.

Fig. 10 Voltage Response at load bus with different controllers

Fig. 11 Voltage Response at load bus for sag clearance with different

controllers

Fig. 12 Voltage Response at load bus for swell clearance with different

controllers



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Fig. 13 Power Response at load bus for sag created

Fig. 14 Power Response at load bus for swell created

VI. CONCLUSION

To improve the power quality of the transmission and

distribution system there are many methods available.

This paper has presented a novel intelligent control

technique for DPFC to improve the power quality. The

Conventional PI controller cannot improve the voltage

profile at a faster rate to the nominal value. The

limitations of the conventional control are rectified first

by the proposed fuzzy control and observation has made

that ANN control has improved the voltage profile at a

faster rate than Fuzzy control. The experimental results

of conventional and intelligent control techniques are

presented with different comparisons of voltage sag,

voltage swell and reactive power at load bus. It is shown

that the DPFC has improved the voltage profile at a faster

rate with ANN control.

REFERENCES

[1] Zhihui Yuan, Sjoerd W. H. de Haan, Jan Braham Ferreira and

Dalibor Cvoric,, “A FACTS Device: Distributed Power–Flow Controller(DPFC),” IEEE transactions on power electronics, VOL. 25, NO. 10, OCTOBER 2010.

[2] Mr. Sandeep R. Gaigowal and Dr. M. M. Renge, “ Some studies of Distributed Series FACTS Controller to control active power flow through Transmission Line,‟‟ 2013,International Conference on Power, Energy and Control (ICPEC), pp 124-128.

[3] K.R.Suja and I Jacob Raglend, “Fuzzy Based Unified Power Quality Conditioner for Power Quality Improvement,” International Conference on Circuits, Power and Computing Technologies[ICCPCT-2013], pp 49-52.

[4] Divya Nair, Ashwini Nambiar, Megha Raveendran and Neenu P. Mohan “Mitigation of Power Quality Issues using Dstatcom,” Emerging trends in Electrical Engineering and Energy management(ICETEEEM), 2012, pp 65-69.

[5] Santosh Kumar Gupta and Shelly Vadhera, “Performance of Distributed Power Flow Controller on System Behaviour under Unbalance Fault Condition,” Energy and Systems (SCES), 2014, pp 1-5.

[6] K.H.Kuypers, R.E.Morrison and S.B.Tennakoon,“Power Quality Implications Associated with a Series FACTS Controller,” IEEE Harmonics and Quality of Power , 2000, pp 176-181, volume1.

[7] Ahmad Jamshidi, S. Masoud Barakati and Mohammad Moradi Ghahderijani, „‟ Power Quality Improvement and Mitigation Case Study Using Distributed Power Flow Controller,‟‟Industrial Electronics (ISIE),2012 pp 464 - 468.

[8] Shital B.Rewatkar, Y.C.C.E Nagpur and Shashikant G.Kewte,‟‟ Role of Power Electronics based FACTS Controller SVC for mitigation of Power Quality Problems, „‟ Emerging trends in Engineering and Technology,(ICETET),2009,pp731–735.

[9] Akhib Khan Bahamani,Dr. G.V.Siva Krishna Rao, Dr. A.A. Powly Thomas and Dr. V.C. Veera Reddy, „‟ Modelling and Digital Simulation of DPFC System using Matlab Simulink,‟‟ International Journal of Engineering Trends in Electrical and Electronics (IJETEE ), vol. 6, Issue 1,August,2013.

[10] Anurag S.D. Rai, Dr. C.S. Rajeshwari and Dr. Anjali Potnis,

“Modeling of Distributed Power Flow Controller (DPFC)in MATLAB / SIMULINK,‟‟ International Journal of

Interdisciplinary Research, Vol. 01, Issue 06, September-2014.

[11] Meenakshi Jain and Ms. Ritu Jain, „‟ Simulink Implementation of Distributed Power Flow Controller,‟‟ International Journal of

Science and Research (IJSR), Vol.3, Issue9, September,2012.

[12] David chapman, „‟power quality application guide,‟‟copper

development association, march 2001.

[13] Michael Negnevitsky, “Artificial Intelligence, A guide to

Intelligent Systems”, a text book for neural networks applied to engineering.



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[14] Akwukwaegbu I. O, Okwe Gerald Ibe,‟‟ Concepts of Reactive

Power Control and Voltage Stability Methods in Power System

Network,‟‟ IOSR Journal of Computer Engineering (IOSR-JCE) Volume 11, Issue 2 (May-Jun 2013), PP 15-25

[15] Ying Bai and Dali Wang,‟‟ fundamentals of Fuzzy Logic Control–

Fuzzy Sets, Fuzzy Rules and Defuzzifications,‟‟ a text book on fuzzy logic applied to engineering

[16] Roger C. Dugan, Mark F. McGranaghan, Surya Santoso, H.Wayne Beaty, “ Electrical Power Systems Quality”, a text book

copyrighted from Mc Graw – Hill.

[17] Robert Babuska, “Fuzzy Modelling for Control”, a C.I.P Catalogue

record, the Library of Congress, ISBN 978-94-011-4868-9, Springer Science + Business Media, New York, Ist Edition, 1998.

Authors’ information

Radhika Dora was born in 1978. She received

her B.Tech degree in Electrical and Electronics Engineering from JNTU Ananthapur in the Year

2000 and M.Tech in Electrical Power Engineering

from JNTU Hyderabad in 2007. She is pursuing Ph.D from JNTU Hyderabad in Electrical and

Electronics Engineering. Currently she is working

as Associate Professor in the Department of Electrical and Electronics Engineering in Geethanjali College of

Engineering and Technology, Cheeryal, Keesara, Hyderabad. Her major

interested areas are Power Quality, Power Converters, Power Electronics etc.

. Dr. Sanker Ram B.V did his B.E in Electrical

Engineering in the year 1982 from OU college of

Engineering. Obtained M.Tech in Power Systems in 1984 from OU College of Engineering and Ph.D in

2003 from JNTU. He joined in JNTU in 1985 and

worked in various Capacities. He has 70 technical papers to his credit in various international and

national journals and conferences. He has guided 12 research scholars

for Ph.D and 6 Candidates are still pursuing their research. His areas of interest include FACTS, Power Electronic Applications to Power

Systems, Power Systems Reliability.

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