Optimal reactive power dispatch problem solved by symbiotic organism search algorithm

This paper introduces a newly developed symbiotic organism search algorithm for deal with ORPD problem. The reactive power optimization problem is essential for suitable operation and regulation in power system network. It helps operators to control the voltage limits, to curtail the real power loss in transmission lines, enhances the strength of the electrical power system to withstand and counteract voltage collapse during load variations in an electrical power system. The symbiotic organism search algorithm is one of the assuring, latest developments in the tract of Meta heuristic algorithms. The texture — animated philosophy of symbiotic organism search algorithm resembles the interactive nature among organisms in feature. Organisms in the real cosmos infrequently live in isolation due to their dependence on other organisms for livelihood and longevity. This ORPD problem is formulated by generator output voltages (continuous variable), regulating transformers and switchable VAR devices (discrete variables). The proposed symbiotic organism search algorithm is realized for IEEE-14 bus and 30-bus systems. The improved result values are compared with Evolutionary programming, Differential evolutionary algorithm, dynamic particle swarm optimization, self-adaptive real coded genetic algorithm and modified Gaussian bare bones teaching learning based optimization.

[1]  Swapan Kumar Goswami,et al.  Active and reactive dispatch with minimum control movements , 2013 .

[2]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[3]  H. Yoshida,et al.  A particle swarm optimization for reactive power and voltage control considering voltage security assessment , 1999, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[4]  K. S. Swarup,et al.  Differential evolutionary algorithm for optimal reactive power dispatch , 2008 .

[5]  S. Granville.  Optimal reactive dispatch through interior point methods , 1994 .

[6]  William F. Tinney,et al.  Optimal Power Flow Solutions , 1968 .

[7]  Mohammad Ali Abido,et al.  Differential evolution algorithm for optimal reactive power dispatch , 2011 .

[8]  C. Bulac,et al.  Reactive power optimization using evolutionary techniques: Differential Evolution and Particle Swarm , 2011, 2011 16th International Conference on Intelligent System Applications to Power Systems.

[9]  M. A. Abido,et al.  Optimal power flow using particle swarm optimization , 2002 .

[10]  V. H. Quintana,et al.  Reactive-Power Dispatch by Successive Quadratic Programming , 1989, IEEE Power Engineering Review.

[11]  W. Tinney,et al.  Optimal Power Flow By Newton Approach , 1984, IEEE Transactions on Power Apparatus and Systems.

[12]  Mahdi Taghizadeh,et al.  Solving optimal reactive power dispatch problem using a novel teaching-learning-based optimization algorithm , 2015, Eng. Appl. Artif. Intell..

[13]  Ulaş Kılıç,et al.  Artificial bee colony algorithm solution for optimal reactive power flow , 2012, Appl. Soft Comput..

[14]  Xianzhong Duan,et al.  Study of differential evolution for optimal reactive power flow , 2007 .

[15]  S. M. Shahidehpour,et al.  An Efficient Technique for Reactive Power Dispatch Using a Revised Linear Programming Approach , 1988 .

[16]  K. Lee,et al.  A United Approach to Optimal Real and Reactive Power Dispatch , 1985, IEEE Transactions on Power Apparatus and Systems.