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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:QU Su-han,Ma Ping,CAI Xing-guo.Chaos quantum particle swarm optimization for reactive power optimization considering voltage stability[J].Journal of Harbin Institute Of Technology(New Series),2010,17(3):351-356.DOI:10.11916/j.issn.1005-9113.2010.03.011.
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Chaos quantum particle swarm optimization for reactive power optimization considering voltage stability
Author NameAffiliation
QU Su-han School of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150001,China 
Ma Ping School of Automation Engineering,Qingdao University,Qingdao 266071,China 
CAI Xing-guo School of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150001,China 
Abstract:
The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. To deal with the problem,quantum particle swarm optimization (QPSO) is firstly introduced in this paper,and according to QPSO,chaotic quantum particle swarm optimization (CQPSO) is presented,which makes use of the randomness,regularity and ergodicity of chaotic variables to improve the quantum particle swarm optimization algorithm. When the swarm is trapped in local minima,a smaller searching space chaos optimization is used to guide the swarm jumping out the local minima. So it can avoid the premature phenomenon and to trap in a local minima of QPSO. The feasibility and efficiency of the proposed algorithm are verified by the results of calculation and simulation for IEEE 14-buses and IEEE 30-buses systems.
Key words:  reactive power optimization  voltage stability margin  quantum particle swarm optimization  chaos optimization
DOI：10.11916/j.issn.1005-9113.2010.03.011
Clc Number:TM714.3
Fund:

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