Fuzzy Based Multi-Objective Evolutionary Algorithm for Optimal Location of FACTS Devices in a Power System

Anju Gupta(1*), P. R. Sharma(2)

(1) University Rohtak, India
(2) University Rohtak, India
(*) Corresponding author


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


This paper presents a Multi Objective Evolutionary Algorithm (MOEA) to solve nonlinear power system optimization problem. The optimization is done with three variables; choice, location and rating of FACTS devices. The targeted technical objectives are; to control the power flow, increase the transmission line capability to its maximum thermal limits, to keep the voltages within limits while minimizing losses. The parameters of objectives are optimized independently and then the formulation of a nonlinear constrained multi-objective optimization problem is done while carrying out concurrent optimization taking two and three objectives. A Fuzzy min max approach is incorporated to reduce the best solution out of non dominated Pareto optimal set. Assessments have been done on  IEEE 14 and IEEE 30 bus system for  different loading conditions with two devices SVC and TCSC modeled in steady state and the results  affirm the potency of the propound  approach.
Copyright © 2013 Praise Worthy Prize - All rights reserved.

Keywords


FACTS; Pareto; Multi-Objective; Optimal

Full Text:

PDF


References


D.J.Gotham and G.T.Heydt,”Power flow control and Power flow

studies for system with FACTS devices ,”IEEE Trans. Power

Systems ,vol 13,no 1,1998

M.A. Al-Biati, M.A. El-Kady,.A.Al-Ohaly, “Dynamic stability improvement via coordination of Static Var Compensator and power system stabilizer control actions,” Electric Power system Research, Volume 58, Issue 1, 21 May 2001, Pages 37–

F.D Galians, K,Almeida, M,Toussaint, J,Groffin and Atanackovic,“Assessment and control of impact of FACTS devices on power system performance,” IEEE Trans.

Power System, Vol 11, no 4, Nov 1996

P. Kessel and H.Glavitsch, july 1996, “Estimating the voltage stability of power system,” IEEE Transaction on Power Delivery, vol.1,No 3 pp346- 352.

C.T.T.Lie and W.Deng,”Optimal flexible AC transmission

systems(FACTS) devices allocation ,”Electrical power and energy Systems,vol 19,No 2,pp 125-134,1997.

S. N. Singh and A.K.David,”Congestion management by

optimizing FACTS Devices location”,Electric Power system

Research vol 58,pp71-79,2001.

S.N.Singh, A.K.David,” A new approach for placement of

FACTS devices in open Power markets,”IEEE Power

Engineering ,Vol 9,pp 58-60.

K.R.C Mamandur and R.D. Chenoweth,” Optimal Control of Reactive Power flow for Improvements in Voltage profiles and for real loss minimization,” IEEE Tran. On Power Apparatus and Systems ,vol PAS-100,No 7,1981,pp 3185-3193.

K.Iba ,” Reactive Power Optimization by Genetic Algorithm,”

IEEE Trans. On Power Ssytem,Vol 9,No 2 1994,pp 685-692.

J.Baskaran V.Palanisamy,”Optimal Location of FACTS device

in a power system network considering power loss using genetic

algorithm”EE pub on lone journal, march 7,2005.

Pisica,C,Bulac,L Toma ,M,Eremia,” Optimal SVC Placement in

Electric Power Ssytems Using a Genetic Algorithm Based

Method” IEEE Bucharest Power Tech Conference,2009.

S.Gerbex,R Cherkaoui and A.J.Germond ,”Optimal Location of

multi-type FACTS devices by means of genetic algoritm,”IEEE

Trans.Power system, vol 16,pp. 537-544,August 2001.

Lijun Cai and Istvan Erlich ,”Optimal Choice and Allocation of

FACTS devices using Genetic Algorithm,”ISAP Intelligent

Systems Application to Power

System,2003,Lemnos,Greece,August 31-september 3 2003.

C.M Fonseca and P.J. Fleming” An overview of Evolutionary

based Multi-objective optimization,” Evolutionary Computation,

Vol 3, No 1 , 1995,pp 1-16.

E.Zitzler and L.Thiele ,”An Evolutionary Algorithm for Multi-

objective optimization :The strength Pareto Approach,” Swiss

Federal Institute of Technology, TIK report ,No 43 ,1998.

C.A.C Coello,”A comprehensive Survey of Evolutionary

algorithm in multiobjective optimization,”Evolutionary

computation,vol 3,No 1 ,1995,pp1-16.

E.Zitzler and L. Thiele, “Multi-objective Evolutionary

Algorithms: A comparative Case Study and the Strength

Pareto Approach,” IEEE Trans. on Evolutionary

Computation, vol.3, no. 4, pp. 257-271, Nov. 1999.

M.A Abido, Member IEEE ,”Multoobjective Optimal VAR Dispatch using Strength Pareto Evolutionary Algorithm,” IEEE congress on Evolutionary Computation, july 2006.

B. Venkatesh, G. Sadasivam and M. Abdullah Khan, “A New

Optimal Power Scheduling Method for Loss Minimization and Voltage Stability Margin Maximization Using Successive Multiobjective Fuzzy LP Technique,” IEEE Trans. on Power Systems, vol. 15, no. 2, pp. 844-851, May. 2000.

H. Dommel and W. Tinney, "Optimal Power Flow Solutions, " IEEE Trans. Power Apparatus and Systems, vol. PAS-87, no10,pp. 866-1876, Oct.. 1968.


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2021 Praise Worthy Prize