A neural network control approach to voltage stability enhancement

P. Rajalakshmi, M. Rathinakumar
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引用次数: 0

Abstract

In this paper the static voltage stability index has been identified by minimum singular value of the power flow Jacobian matrix. The system security in power system models by the scalar magnitude of a voltage stability index may be a very difficult task. When we are taking account of reactive power generation limits it is very difficult to predict stability index at voltage collapse points. Here a quick method is used to calculate the minimum singular value and the corresponding left and right singular vectors are presented. This developed algorithm is very useful in online because it required small amount of computation time. For different patterns of load and generation are increases, to determine voltage collapse point. For solving the maximum loading problem an optimal power flow approach was used with these points the MSV is calculated and used for training and testing the Neural Network.
增强电压稳定性的神经网络控制方法
本文利用潮流雅可比矩阵的最小奇异值来确定静态电压稳定指标。在电力系统模型中,用电压稳定指标的标量大小来确定系统的安全性是一项非常困难的任务。当考虑无功发电限制时,很难预测电压崩溃点的稳定指标。本文提出了一种快速计算最小奇异值的方法,并给出了相应的左、右奇异向量。该算法的计算量小,在在线应用中非常有用。对于不同的负荷模式和发电量都是增加的,以确定电压的崩溃点。为了解决最大负荷问题,采用最优潮流法,计算了这些点的MSV,并将其用于神经网络的训练和测试。
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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