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Artificial neural net approach for capacitor placement in power system

机译:电力系统电容器放置的人工神经网络方法

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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.
机译:作者提出了一种新方法,用于使用三层前馈神经网络控制电力系统中的多型电容器。在所提出的方案中,神经网络是用两种算法分别培训的,即反正验证和一个组合的BackProjagation-Cauchy的学习算法。对30个总线IEEE测试系统进行研究,并获得了非常令人满意的结果。网络的输入是少数选定总线的实际功率,无功功率和电压幅度,网络的输出是电容式VAR注入的值。在两个算法之间进行性能比较,发现综合的BackPropagation-Cauchy的算法比另一算法更好。

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