电力系统中电容器布置的人工神经网络方法

P. Dash, S. Saha, P. Nanda
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引用次数: 15

摘要

提出了一种利用三层前馈神经网络控制电力系统中多分接电容器的新方法。在该方案中,神经网络分别使用两种算法进行训练,即反向传播算法和联合反向传播-柯西学习算法。对30总线的IEEE测试系统进行了研究,取得了满意的结果。电网的输入是选定的几个母线上的实际功率、无功功率和电压幅值,网络的输出是电容式无功注入的值。比较了两种算法的性能,发现反向传播-柯西联合算法优于另一种算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial neural net approach for capacitor placement in power system
The authors propose a new methodology for controlling multitap capacitors in a power system using a three layer feedforward neural network. The neural network, in the proposed scheme is separately trained with two algorithms namely backpropagation and a combined backpropagation-Cauchy's learning algorithm. Studies on 30 bus IEEE test system are carried out and quite satisfactory results are obtained. The inputs to the net are the real power, reactive power and voltage magnitude at a few selected buses and the network's outputs are the values of capacitive Var injection. Performance comparison is made between two algorithms and the combined backpropagation-Cauchy's algorithm is found to be better than the other.<>
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