Harmonic elimination and reactive power compensation through a shunt active power filter by twin neural networks with predictive and adaptive properties

A. Bhattacharya, C. Chakraborty
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引用次数: 9

Abstract

A method for controlling an active power filter using Artificial Neural Network(ANN) is presented in this paper. This paper applies ANN based predictive and adaptive reference generation technique. Predictive scheme extracts the information of the fundamental component through an ANN that replaces a low pass filter. This ANN based low pass-filter is trained offline with large number of training set to predict the fundamental magnitude of load current. This predictive reference generation technique works well for clean source voltage. However, the performance deteriorates in case of distortion in source voltage and also with noise. To overcome this, an Adaline based ANN is applied after the operation of the predictive algorithm. It has been shown that the combined predictive-adaptive approach offers better performance. Simulation results and experimental results are presented to confirm the usefulness of the proposed technique..
利用具有预测和自适应特性的双神经网络对并联有源滤波器进行谐波消除和无功补偿
提出了一种利用人工神经网络控制有源电力滤波器的方法。本文采用了基于人工神经网络的预测和自适应参考生成技术。预测方案通过替代低通滤波器的人工神经网络提取基元信息。这种基于人工神经网络的低通滤波器通过大量的训练集进行离线训练来预测负载电流的基本大小。这种预测基准生成技术适用于清洁电源电压。但是,在源电压失真和噪声的情况下,性能会下降。为了克服这一问题,在对预测算法进行运算后,应用了基于Adaline的人工神经网络。研究表明,结合预测-自适应方法可以提供更好的性能。仿真结果和实验结果验证了该方法的有效性。
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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