A Novel ANN Based Method for Online Voltage Stability Assessment

B. Suthar, R. Balasubramanian
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引用次数: 11

Abstract

This paper presents an ANN based method for online voltage stability assessment of power systems. The most vulnerable load buses of the system from voltage stability point of view have been identified by Modal analysis. A separate feed forward type of ANN is trained for each vulnerable load bus. For each of these ANN's, some novel inputs, comprising of the moments obtained by multiplying the real power and reactive power contributions with the electrical distance between each generator-vulnerable load bus pair and the reactive power margins available at the generators, are used in addition to the usually used inputs viz. the real and reactive power loads and the voltage magnitude at the vulnerable load bus. The target output for each input pattern is obtained by computing the distance to voltage collapse from the current system operating point using a continuation power flow type algorithm (Contour Program) incorporating the Q limits of the generators. The proposed method has been applied to the IEEE 30 bus test system. The distances to voltage collapse obtained by the ANN and by the analytical method are found to be closely matching with each other.
一种基于神经网络的在线电压稳定性评估新方法
提出了一种基于神经网络的电力系统电压在线稳定评估方法。通过模态分析,从电压稳定的角度确定了系统中最脆弱的负载母线。针对每个脆弱负载总线分别训练一种前馈神经网络。对于这些人工神经网络,除了通常使用的输入,即实际和无功负载以及脆弱负载母线上的电压幅值之外,还使用了一些新的输入,这些输入由实功率和无功功率贡献乘以每个发电机-脆弱负载母线对之间的电距离和发电机可用的无功裕度所获得的力矩组成。每个输入模式的目标输出是通过使用结合发电机Q限的连续潮流算法(轮廓程序)计算从当前系统工作点到电压崩溃的距离来获得的。该方法已在ieee30总线测试系统中得到应用。结果表明,人工神经网络得到的电压崩溃距离与解析法得到的电压崩溃距离基本吻合。
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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