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Advances in Machine Learning and Cybernetics

Volume 3930 of the series Lecture Notes in Computer Science pp 908-917

A Hybrid Genetic Algorithm/Particle Swarm Approach for Evaluation of Power Flow in Electric Network

  • T. O. TingAffiliated withCarnegie Mellon UniversityComputational Intelligence Applications Research Laboratory, Department of Electrical Engineering, The Hong Kong Polytechnic University
  • , K. P. WongAffiliated withCarnegie Mellon UniversityComputational Intelligence Applications Research Laboratory, Department of Electrical Engineering, The Hong Kong Polytechnic University
  • , C. Y. ChungAffiliated withCarnegie Mellon UniversityComputational Intelligence Applications Research Laboratory, Department of Electrical Engineering, The Hong Kong Polytechnic University

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Abstract

This paper presents an investigation of possible hybrid genetic algorithm / particle swarm optimization approaches to evaluate the flow of electric power in power transmission network. The possible schemes are presented and their performances are illustrated by applying them to the power flow problem of the Klos Kerner 11-busbar system. The performance of the hybrid algorithm in terms of reliability is further improved by applying the optimal values for both inertia weight and mutation probability which are found through parameter sensitivity analyses.