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| Application of Improved Particle Swarm Optimization Algorithm to Multi-Objective Reactive Power Optimization |
| Li Xinbin1, 2, Zhu Qingjun1 |
| 1. Yanshan University Qinhuangdao 066004 China 2. Hebei Mathematical Research Center Shijiazhuang 050000 China |
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Abstract In order to avoid the defect that a conventional particle swarm optimization (PSO) algorithm is easy to trap into a local optimization, a new fuzzy adaptive-simulated annealing PSO algorithm is proposed in this paper. Based on the principle of fuzzy logic, the inputs to the fuzzy controller are the normalized current best performance valuation, inertia weighing of the PSO algorithm and the learning factor, the outputs of the controller are the parameters rate of change. The fuzzy rules are formulated based on the experience of parameters settings so as to adjust the PSO parameters adaptively. The quality of particles’ new location after the adjustment is valued by simulated annealing (SA). Then, the modified PSO algorithm is introduced to solve multi-objective reactive power optimization problem. IEEE 30-bus and IEEE118-bus system are simulated to verify the effectiveness and feasibility of SA- fuzzy self-adaptive particle swarm optimization algorithm.
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Received: 10 July 2009
Published: 04 March 2014
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2010, Vol. 25 
