A comparative study of evolutionary algorithms for phase shifting transformer setting optimization

The European transmission system operators of today have to struggle with a general increase in power flows, leading to more and more to line congestions in certain transmission corridors. One reasonable countermeasure for this is active power flow control using phase shifting transformers (PST). To avoid negative interdependencies between these PSTs in different European control areas, the need for coordination of these power flow controlling is expected to rise. This paper uses sever-al variants of Differential Evolution, Genetic Algorithm, Mean Variance Mapping Optimization and Particle Swarm Optimization to solve a PST optimization problem in the IEEE 57-Bus System. With help of the results, the algorithms are compared with respect to the average fitness, standard deviation, computation time and necessary number of iterations. The investigation shows that the attractive and repulsive Particle Swarm Optimization (ARPSO) performs as good as Differential Evolution and are consequently most suitable to solve the PST optimization problem.

[1]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[2]  Jacques Riget,et al.  A Diversity-Guided Particle Swarm Optimizer - the ARPSO , 2002 .

[3]  R. Belmans,et al.  A literature survey of Optimal Power Flow problems in the electricity market context , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[4]  Mustafa Bagriyanik,et al.  A fuzzy-genetic algorithm based approach for controlling unscheduled flows using SC and PST , 2011, 2011 North American Power Symposium.

[5]  István Erlich,et al.  A Mean-Variance Optimization algorithm , 2010, IEEE Congress on Evolutionary Computation.

[6]  John Geraghty,et al.  Genetic Algorithm Performance with Different Selection Strategies in Solving TSP , 2011 .

[7]  Rakesh Angira,et al.  A Comparative Study of Differential Evolution Algorithms for Estimation of Kinetic Parameters , 2012 .

[8]  Josef Tlusty,et al.  A novel weight-improved particle swarm optimization algorithm for optimal power flow and economic load dispatch problems , 2010, IEEE PES T&D 2010.

[9]  René Thomsen,et al.  A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[10]  Habibollah Haron,et al.  Performance comparison of Genetic Algorithm, Differential Evolution and Particle Swarm Optimization towards benchmark functions , 2013, 2013 IEEE Conference on Open Systems (ICOS).

[11]  Wil L. Kling,et al.  Phase shifter coordination for optimal transmission capacity using particle swarm optimization , 2008 .

[12]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[13]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[14]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[15]  István Erlich,et al.  Evaluation of the mean-variance mapping optimization for solving multimodal problems , 2013, 2013 IEEE Symposium on Swarm Intelligence (SIS).