Power system topological observability analysis using artificial neural networks

被引：0
|
作者：
Jain, A [1 ]
Balasubramanian, R [1 ]
Tripathy, SC [1 ]
Singh, BN [1 ]
Kawazoe, Y [1 ]
机构：
[1] Tohoku Univ, Inst Mat Res, Sendai, Miyagi 9808577, Japan
来源：
2005 IEEE POWER ENGINEERING SOCIETY GENERAL MEETING, VOLS, 1-3 | 2005年
关键词：
artificial neural network; back propagation; quickprop; state estimation; topological observability;
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：
This paper presents a new method for the power system topological observability analysis using the artificial neural networks. The power system observability problem, related to the power system configuration or network topology, called as the topological observability, is studied utilizing the artificial neural network model, based on multilayer perceptrons using the Back-propagation algorithm as the training algorithm. Another training algorithm, quickprop is also applied for training the similar artificial neural network to further check the suitability of other training algorithm also. The proposed artificial forward neural network model has been tested on sample power systems and results are presented.
引用
收藏
页码：497 / 502
页数：6
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