针对特征选择问题的改进蚁群算法及其在电力系统安全评估中的应用

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Abstract A novel embedded feature selection method based on improved ant colony optimization algorithm combined with the k-nearest neighbor (k-NN) classifier is proposed to tackle the feature selection problem. A weighted sum of the k-NN classification accuracy and the selected feature dimension form the fitness function. The improved ant colony optimization algorithm provides good global searching capability and multiple sub-optimal solutions. A local refinement searching scheme is designed to exclude the redundant features and improves the convergence rate. The feasibility and effectiveness of the proposed algorithm is first verified by a set of artificial test data and then applied to the power system security analysis problem. In the IEEE 10-unit-39-bus system, the proposed scheme obtains well-behaved security-related kernel features and provides good transient stability assessment performance.

Key words： Feature selection ant colony optimization algorithm k-nearest neighbor classifier power system security analysis

Received: 14 January 2009 Published: 04 March 2014

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TM712

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	Zhang Xiaoqiang
	Guan Lin
	Wang Tongwen

Cite this article:

Zhang Xiaoqiang,Guan Lin,Wang Tongwen. Kernel Feature Identification Based on Improved Ant Colony Optimization Algorithm for Transient Stability Assessment[J]. Transactions of China Electrotechnical Society, 2010, 25(12): 154-160.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2010/V25/I12/154

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