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《Transactions of China Electrotechnical Society》 2006-07
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UPFC in Multi-Machine Power Systems Based on Immune Genetic Algorithm

Jiang Quanyuan Zou Zhenyu Cao Yijia (Zhejiang University Hangzhou 310027 China)  
A novel optimization algorithm—— immune genetic algorithm (IGA) is proposed to design an Unified Power Flow Controller (UPFC) for improing the stability of multi-machine power systems. The proposed IGA is based on immune genetic theory of creature and can simulate the immune system and its behavior of organism, such as second-time immune response, immune memory, immune selection, concentration control and metabolism. Compared with common genetic algorithms, the proposed IGA adopts the following techniques to improve the global searching ability and converge speed: gene recombination, gene mutation, isolated niche and chaos production. Several typical functions are used to verify the excellent performance of IGA. Finally, IGA is applied to optimize the parameters of UPFC to improve the stability of the New England Test Power System (NETPS). Numerical simulation results demonstrate the validity of the optimized UPFC controller.
【Fund】: 国家自然科学基金资助项目(50507018)
【CateGory Index】: TM761
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