| 群体智能的系统辨识 |
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| 引用本文： | 陈文雯,;刘友宽,;孙建平.群体智能的系统辨识[J].云南电力技术,2014(3):10-14. |
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| 作者姓名： | 陈文雯 ;刘友宽 ;孙建平 |
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| 作者单位： | [1]华北电力大学自动化系,河北保定071000; [2]华北电力大学云南电网公司研究生工作站,昆明650217; [3]云南电网公司电力研究院,昆明650217 |
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| 摘 要： | 概括了系统辨识的方法,重点介绍了最小二乘法、群体智能算法中的粒子群算法和改进的粒子群算法,给出了估计模型的选择方法,并结合某1000MW火电机组实例,运用两种方法进行了系统辨识和仿真.仿真结果表明,最小二乘法可以完成对系统的辨识,但存在较大偏差;采用粒子群算法辨识结果良好.
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| 关 键 词： | 系统辨识 最小二乘法 粒子群算法 仿真 |
System Identification Based on Swarm Intelligence |
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| Affiliation: | CHEN Wenwen, LIU Youkuan, SUN Jianping ( 1. Department of Automation, North China Electric Power University, Baoding, Hebei 071000 ; 2. North China Electric Power University Graduate Student Workstations of Yunnan Power Grid Corporation, Kunming 650217; 3. Smart Grid Department of Yunnan Electric Power Research Institute, Kunming 650217 ) |
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| Abstract: | Establishment of the system model is necessary when it comes to study the control system. Therefore, system identifica- tion plays a crucial role in the study of control system, of which the essences are structural optimization and parameter optimization. The article summarizes the methods of system identification, focusing on the least squares method, the particle swarm optimization （PSO） which included in swarm intelligence algorithm and the improved particle swarm optimization, gives the selection method of estimation model and combine with a IO00MW thermal power instance, use two methods to identify and simulate the control system. Simulation results show that the least squares method of identification can be done on the system, but there is a big deviation and it can get good recognition results by using particle swarm optimization. |
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| Keywords: | System Identification Least Squares PSO Simulation |
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