Neural network application to state estimation computation

Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI:10.1109/ANN.1991.213480

T. Nakagawa, Y. Hayashi, S. Iwamoto

引用次数: 37

Abstract

In power systems state estimation computation takes an important role in security controls, and the weighted least squares method and the fast decoupled method are widely-used at present. State estimation computation using the existing Von-Neumann type computer is reaching a limit as far as the solution techniques are concerned, and it is very difficult to expect much faster methods. In order to solve the problem, the authors employ a neural network theory, the Hopfield network theory, which has an ultra parallel algorithm and is different from the existing calculating algorithms, for state estimation computation. A feasibility study using a 6 bus system is shown.<>

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神经网络在状态估计计算中的应用

在电力系统中，状态估计计算在安全控制中起着重要的作用，目前最常用的方法是加权最小二乘法和快速解耦法。就求解技术而言，使用现有的冯-诺伊曼型计算机进行状态估计计算已经达到了极限，并且很难期望更快的方法。为了解决这一问题，作者采用神经网络理论——Hopfield网络理论进行状态估计计算，该理论与现有的计算算法不同，具有超并行算法。展示了采用6总线系统的可行性研究

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the First International Forum on Applications of Neural Networks to Power Systems

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