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基于自组织神经网络和稳态模型的多台感应电动机聚合方法
引用本文:张景超,张承学,鄢安河,张鹏飞,李奎.基于自组织神经网络和稳态模型的多台感应电动机聚合方法[J].电力系统自动化,2007,31(11):44-48.
作者姓名:张景超  张承学  鄢安河  张鹏飞  李奎
作者单位:1. 武汉大学电气工程学院,湖北省武汉市,430072;河南省电力公司,河南省郑州市,450052
2. 武汉大学电气工程学院,湖北省武汉市,430072
3. 河南省电力公司,河南省郑州市,450052
摘    要:电力系统稳定计算中,精确的负荷模型为计算结果的可信度提供了保证。在利用统计综合法进行负荷建模时,如何提高多台感应电动机的聚合精度是研究的重要内容之一。文中提出一种基于自组织神经网络对电动机进行分类、并针对同一类型的电动机采用稳态模型进行等值的聚合方法。最后,应用中国电力科学研究院开发的PSD-BPA暂态稳定计算程序,将分类聚合前后的典型居民和典型商业电动机数据分别代入进行仿真计算。结果表明,采用所提出的聚合方法可以提高多台电动机的聚合精度。

关 键 词:稳态负荷模型  电动机聚合  自组织神经网络  负荷建模
收稿时间:2006/10/12 0:00:00
修稿时间:5/21/2007 9:52:04 AM

Aggregation of Multi Induction Motors Based on the Self-organized Neural Network and Steady State Model
ZHANG Jingchao,ZHANG Chengxue,YAN Anhe,ZHANG Pengfei,LI Kui.Aggregation of Multi Induction Motors Based on the Self-organized Neural Network and Steady State Model[J].Automation of Electric Power Systems,2007,31(11):44-48.
Authors:ZHANG Jingchao  ZHANG Chengxue  YAN Anhe  ZHANG Pengfei  LI Kui
Affiliation:1. Wuhan University, Wuhan 430072, China;2. Henan Electric Power Company, Zhengzhou 450052, China
Abstract:In power system stability computation, accurate load models can ensure the reliability of computed results. Improving the aggregation precision of multi induction motors is one of the most important contents in load modeling with the component-based approach. In this paper, an aggregation method is presented, in which the multi induction motors are classified into different types using the self-organized neural network and each type of motor is aggregated with a new steady state model equivalence method. Finally, simulations are made on the typical residential and commercial induction motors data in IEEE 9 grid with the PSD-BPA program developed by CEPRI. The results have proved the validity of the method proposed.
Keywords:steady state load model  aggregation of induction motors  self-organized neural network  load modeling
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