Journal of South China University of Technology (Natural Science Edition) ›› 2010, Vol. 38 ›› Issue (3): 89-94，100.doi: 10.3969/j.issn.1000-565X.2010.03.016

• Power & Electrical Engineering • Previous Articles     Next Articles

Selection of Kernel Input Features and Evaluation Rules for Transient Stability Assessment

Guan Lin1  Zheng Chuan-cai2  Wang Lu3  Zhang Xiao-qiang1  Wang Tong-wen1   

  1. 1. School of Electric Power, South China University of Technology, Guangzhou 510640, Guangdong, China; 2. Guangzhou Electric Power Supply Bureau, Guangzhou 510620, Guangdong, China; 3. Wuxi Electric Power Supply Bureau, Wuxi 214000, Jiangsu, China
  • Received:2009-03-12 Revised:2009-10-13 Online:2010-03-25 Published:2010-03-25
  • Contact: 管霖（1970-），女，教授，博士生导师，主要从事电网稳定与控制、电网规划、人工智能在电力系统中的应用研究． E-mail:lguan@scut．edu．cn
  • About author:管霖（1970-），女，教授，博士生导师，主要从事电网稳定与控制、电网规划、人工智能在电力系统中的应用研究．
  • Supported by:

    国家自然科学基金资助项目（50407014）

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Abstract:

This paper deals with two key issuses of the artificial intelligence （AI） -based transient stability assess- ment （TSA） of power system, namely the selection of kernel input features and the stability-related evaluation mo- del. In the investigation, first, data-driven feature selection method and rule extraction algorithm are proposed. Then, the key features are evaluated and the transient stability rules are made from the training samples. During the feature selection, a genetic algorithm-based k-nearest neighbor （GA-knn） is used to assess the input features. Du- ring the rule extraction, a mining algorithm of classification and association rules is followed to form the rules of transient stability assessment. The proposed method is then applied to both the New England 10-machine 39-bus and the 3-machine 9-bus systems, and the results are compared and analyzed. It is found out that the selected ker- nel features from 53 candidates and the obtained rules are adapted for the two test power systems. However in the stability boundary, evaluation rules are complex and specific.

Key words: power system, transient stability assessment, kernel input feature, knowledge extraction, rule analysis