Implementation of Imperialist Competitive Algorithm for optimal allocation of FACTS devices to enhance the power system performance

This paper deals with the implementation of Imperialist Competitive Algorithm (ICA) in determining the optimal location and optimal control parameters of TCSC and STATCOM devices for enhancing the power system performance. The power system loadability is increased to its maximum capability or the system total real power loss is reduced keeping the voltage stability of all buses and thermal stability of all transmission lines within the specified limits. The proposed algorithm is being tested in an Indian practical utility Neyveli Thermal Power plant (NTPS) 23 bus system and the results are compared with other intelligent heuristic algorithms.

[1]  R. Vanitha,et al.  A Fuzzy Based Evolutionary Algorithm for Solving Multiobjective Optimal Power Flow with FACTS Devices , 2015 .

[2]  J. Baskaran,et al.  Optimal location of FACTS devices in a power system solved by a hybrid approach , 2006 .

[3]  M. Basu,et al.  Multi-objective optimal power flow with FACTS devices , 2011 .

[4]  Yankui Zhang,et al.  Power injection model of STATCOM with control and operating limit for power flow and voltage stability analysis , 2006 .

[5]  P Ajay D Vimal Raj Performance evaluation of swarm intelligence based power system Optimization strategies , 2008 .

[6]  Rajiv K. Varma,et al.  Thyristor-Based Facts Controllers for Electrical Transmission Systems , 2002 .

[7]  Stephen A. Sebo,et al.  Thyristor-Based FACTS Controllers for Electrical Transmission Systems , 2002 .

[8]  Sahand Ghavidel,et al.  Application of imperialist competitive algorithm with its modified techniques for multi-objective optimal power flow problem: A comparative study , 2014, Inf. Sci..

[9]  E. S. Ali,et al.  Imperialist competitive algorithm for optimal STATCOM design in a multimachine power system , 2016 .

[10]  Hossein Ranjbar,et al.  Imperialist competitive algorithm based optimal power flow , 2014, 2014 22nd Iranian Conference on Electrical Engineering (ICEE).

[11]  K. Chandrasekaran,et al.  Touring Ant colony Optimization technique for Optimal Power Flow incorporating thyristor controlled series compensator , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[12]  Sahand Ghavidel,et al.  A novel hybrid algorithm of imperialist competitive algorithm and teaching learning algorithm for optimal power flow problem with non-smooth cost functions , 2014, Eng. Appl. Artif. Intell..

[13]  J. Baskaran,et al.  Multi Objective Optimal Power Flow with STATCOM Using DE in WAFGP , 2015 .

[14]  Ravindra Arora,et al.  IEEE Press Series on Power Engineering , 2011 .

[15]  G. Selvakumar,et al.  Hybrid Real coded Genetic Algorithm - Differential Evolution for Optimal Power Flow , 2013 .