Open AccessContingency‐Constrained Optimal Power Flow Using Simplex‐Based Chaotic‐PSO Algorithm
Author(s) -
Zwe-Lee Gaing,
Chia-Hung Lin
Publication year - 2011
Publication title -
applied computational intelligence and soft computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 10
eISSN - 1687-9732
pISSN - 1687-9724
DOI - 10.1155/2011/942672
Subject(s) - chaotic , computer science , particle swarm optimization , mathematical optimization , power flow , robustness (evolution) , maxima and minima , simplex algorithm , convergence (economics) , simplex , swarm behaviour , electric power system , economic dispatch , algorithm , power (physics) , linear programming , mathematics , artificial intelligence , mathematical analysis , biochemistry , physics , chemistry , geometry , quantum mechanics , economics , gene , economic growth
This paper proposes solving contingency-constrained optimal power flow (CC-OPF) by a simplex-based chaotic particle swarm optimization (SCPSO). The associated objective of CC-OPF with the considered valve-point loading effects of generators is to minimize the total generation cost, to reduce transmission loss, and to improve the bus-voltage profile under normal or postcontingent states. The proposed SCPSO method, which involves the chaotic map and the downhill simplex search, can avoid the premature convergence of PSO and escape local minima. The effectiveness of the proposed method is demonstrated in two power systems with contingency constraints and compared with other stochastic techniques in terms of solution quality and convergence rate. The experimental results show that the SCPSO-based CC-OPF method has suitable mutation schemes, thus showing robustness and effectiveness in solving contingency-constrained OPF problems
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