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A New BP Neural Network Based Method for Load Harmonic Current Assessment

机译:一种新的BP基于BP神经网络的负载谐波电流评估方法

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This paper proposed a new BP Neural Network (BPNN) based method for load harmonic current assessment where the nonlinearities of electricity loads have been modeled based on differential equations. With the trained BPNN, the load current due to fundamental voltage inputs can be well estimated and used to assess the harmonics components subsequently. The simulation results demonstrate that the proposed method can effectively estimate the total harmonic distortion of the load current when the supplied voltage is within the normal range of harmonic limits. The results also prove that the load harmonic current is nearly independent of load capacity and applied voltage, indicating its effectiveness to distinguish the responsibilities of harmonic pollution between the grid and load.
机译:本文提出了一种基于BP神经网络(BPNN)的负载谐波电流评估方法,其中基于微分方程建模了电负载的非线性。通过训练的BPNN,可以估计很好地估计引起的电压输入引起的负载电流并用于随后评估谐波组件。仿真结果表明,当所提供的电压在正常谐波限度范围内时,所提出的方法可以有效地估计负载电流的总谐波失真。结果还证明,负载谐波电流几乎与负载容量和施加电压无关,表明其有效性地区分网格与负载之间谐波污染责任。

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