Modern Heuristical Optimization Techniques for Power System State Estimation


H. A. Tokel, G. Alirezaei, R. Mathar,


        The development of efficient and accurate algorithms for state estimation has come into the focus in power system research as the power grid becomes more decentralized. In this work, we apply the heuristical continuous optimization techniques differential evolution, simulated annealing and particle swarm optimization to power system state estimation problem, and provide a comparison between them in terms of convergence and optimality. Examining the results, we propose a hybrid algorithm combining particle swarm optimization and differential evolution. ~


simulated annealing,particle swarm optimization,differential evolution,power system,meta-heuristic

BibTEX Reference Entry 

	author = {Halil Alper Tokel and Gholamreza Alirezaei and Rudolf Mathar},
	title = "Modern Heuristical Optimization Techniques for Power System State Estimation",
	booktitle = "The 2016 International Conference on Swarm Intelligence Based Optimization",
	address = {Mulhouse, France},
	month = Jun,
	year = 2016,
	hsb =  RWTH-2016-05779,


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