Machine learning approach to power system dynamic security analysis

被引：0

作者：

Niimura, T ^{[1
]}

Ko, HS ^{[1
]}

Xu, H ^{[1
]}

Moshref, A ^{[1
]}

Morison, K ^{[1
]}

机构：

[1] Dept Elect & Comp Engn, Vancouver, BC V6T 1Z4, Canada

来源：

2004 IEEE PES POWER SYSTEMS CONFERENCE & EXPOSITION, VOLS 1 - 3 | 2004年

关键词：

power system security; transient stability; pattern recognition; neural networks; clustering;

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：

In this paper, the authors present a pattern-learning/recognition approach for dynamic security classification using neural networks with a limited number of input data. The input is a set of data representing the pre-contingency power system state (voltages, angles, etc.), and the output is the possible system status (stable/unstable) after contingency. Data clustering is applied to reduce the number of input representing the cases. The reduced input data are then used to train the neural network that learns the input patterns for a possible post-contingency status. The overall accuracy of the classification is considered to be reasonable for a practical-scale power system application.

引用

下载

页码：1084 / 1088

页数：5

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