A Hopfield neural network based approach for state estimation of power systems embedded with FACTS devices

被引：0

作者：

Singh, Satish Kumar ^{[1
]}

Sharma, Jaydev ^{[2
]}

机构：

[1] ABB Ltd, Corp R&D, Vadodara, Gujarat, India

[2] Indian Inst Technol, Dept Elect Engn, Roorkee 110016, Uttar Pradesh, India

来源：

2006 IEEE POWER INDIA CONFERENCE, VOLS 1 AND 2 | 2006年

关键词：

FACTS Device; Hopfield Neural Network; nonlinear programming; state estimation;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Flexible A.C. transmission systems (FACTS) are being used more in large power systems for their significance in manipulating line power flows. Traditional state estimation methods without integrating FACTS devices will not be suitable for power systems embedded with FACTS. In this paper the state estimation of power systems in presence of FACTS devices is presented. Hopfield neural network is simulated as an optimization tool to solve the power system state estimation problem.

引用

页码：345 / +

页数：2

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