A new BVM based approach to transient security assessment

被引：2

作者：

Mohammadi, M. ^{[1
]}

Gharehpetian, G. B. ^{[2
]}

Raoofat, M. ^{[1
]}

机构：

[1] Shiraz Univ, Sch Elect & Comp Engn, Shiraz, Iran

[2] Amirkabir Univ Technol, Dept Elect Engn, Tehran, Iran

来源：

EUROPEAN TRANSACTIONS ON ELECTRICAL POWER | 2010年 / 20卷 / 08期

关键词：

ball vector machines (BVM); transient stability assessment; machine learning; feature selection; SUPPORT VECTOR MACHINES; POWER-SYSTEM SECURITY; EQUAL-AREA CRITERION; STABILITY ANALYSIS; NEURAL-NETWORKS;

D O I：

10.1002/etep.394

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Machine Learning techniques have been extensively used in Power Systems Analysis during the last years. This paper describes a ball vector machine based algorithm for on-line transient security assessment of large-scale power systems. The proposed ball vector machine based security assessment reduces the training time and space complexities in comparison with support vector machines, artificial neural networks, and other machine learning based algorithms. In addition, the proposed algorithm has less support vectors and therefore is faster than existing algorithms for -line applications. A new decision tree based feature selection technique has been presented, too. The proposed ball vector machine based,algorithm has been applied to New England 39-bus test system. The simulation results show the effectiveness of the proposed algorithm for on-line transient security assessment procedure. Copyright (C) 2010 John Wiley & Sons, Ltd.

引用

页码：1163 / 1176

页数：14

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