基于Hopfield神经网络的嵌入式FACTS系统状态估计方法

2006 IEEE Power India Conference Pub Date : 2006-06-05 DOI:10.1109/POWERI.2006.1632574

S.K. Singh, J. Sharma

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引用次数: 14

摘要

柔性交流输电系统由于其在控制线路潮流方面的重要作用，在大型电力系统中得到了越来越多的应用。传统的不集成事实器件的状态估计方法不适用于嵌入事实器件的电力系统。本文研究了存在FACTS装置的电力系统的状态估计问题。仿真了Hopfield神经网络作为解决电力系统状态估计问题的优化工具

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

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

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2006 IEEE Power India Conference

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