Advances in Machine Learning and Cybernetics pp 908-917
A Hybrid Genetic Algorithm/Particle Swarm Approach for Evaluation of Power Flow in Electric Network
- Cite this paper as:
- Ting T.O., Wong K.P., Chung C.Y. (2006) A Hybrid Genetic Algorithm/Particle Swarm Approach for Evaluation of Power Flow in Electric Network. In: Yeung D.S., Liu ZQ., Wang XZ., Yan H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science, vol 3930. Springer, Berlin, Heidelberg
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.
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