Load Shedding against Short-Term Voltage Instability Using Random Subspace Based SVM Ensembles

被引:0
作者
Zhu, Lipeng [1 ]
Lu, Chao [1 ]
Han, Yingduo [1 ]
机构
[1] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China
来源
2017 IEEE POWER & ENERGY SOCIETY GENERAL MEETING | 2017年
基金
中国国家自然科学基金;
关键词
Load shedding; ensemble learning; random subspace; shapelet transform; short-term voltage stability; POWER-SYSTEMS;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Dedicated to response based corrective control against short-term voltage instability, this paper develops an adaptive load shedding approach via random subspace based SVM ensemble (RS-SVME) learning Post-contingency time series (TS) data is collected to implement contingency-independent learning. Shapelet transform is then performed to transform the TS data into a tractable single-valued form. With the transformed data, RS-SVME learning is conducted to construct a stability margin estimator. During the learning process, cost-sensitive concerns regarding misdetection and false alarm are tactfully treated. The correlation based feature selection (CFS) method is employed to help the estimator determine the location and amount of load shedding. Test results on the realistic Hong Kong power grid demonstrate the effectiveness of the whole approach.
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页数:5
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