| 基于微粒群辨识算法的电力负荷综合模型的建模与仿真 |
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| 引用本文： | 谷鹏,石国萍. 基于微粒群辨识算法的电力负荷综合模型的建模与仿真[J]. 江苏电机工程, 2011, 30(2): 41-44 |
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| 作者姓名： | 谷鹏 石国萍 |
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| 作者单位： | 山东电力建设第一工程公司,山东,济南,250100 |
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| 摘 要： | 微粒群优化(PSO)算法具有全局性能好、搜索效率高等优点.应用该算法进行电力系统负荷模型的参数辨识,辩识结果表明PSO算法在计算时间、全局性方面均有比较明显的优势.辨识的模型具有较高精确性,最后通过工程实例进行仿真实验,实验结果验证了模型和算法的有效性.
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| 关 键 词： | 电力系统 负荷模型 微粒群算法 参数辨识 |
Power System Load Modeling and Simulation Based On PSO Algorithm |
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| GU Peng,SHI Guo-ping. Power System Load Modeling and Simulation Based On PSO Algorithm[J]. Jiangsu Electrical Engineering, 2011, 30(2): 41-44 |
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| Authors: | GU Peng SHI Guo-ping |
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| Affiliation: | GU Peng1,SHI Guo-ping2(1.Shandong Electric Power Construction No.1 Company,Jinan Shandong 250100,China,2.School of Information and Electric Engineering,Shandong Jianzhu University,Jinan Shandong 250101,China) |
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| Abstract: | This paper introduced particle swarm optimization(PSO) algorithm,which is efficient and quite immune to local optima.The paper applies PSO algorithm to electrical load parameter identification,and the results verify that PSO algorithm is fairly good in both efficiency and global superiority.The load model based on the algorithm is of high accuracy.In the end,simulation experiments of engineering example are carried on,and the results confirm the availability of both PSO algorithm and the load model. |
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| Keywords: | power systems load modeling PSO algorithm parameter identification |
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