A Multi-layer neural network and an adaptive linear combiner for on-line harmonic tracking

A. Zouidi, F. Fnaiech, K. Al-haddad
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引用次数: 3

Abstract

Intelligent techniques of harmonic detection or estimation are nowadays of a great interest in power system applications, their ability to deal with high non-linearities attract researchers to investigate the performance of these methods mainly based on artificial intelligence namely using artificial neural networks. In the literature many harmonic detection or estimation methods were presented, in this paper we focus on a new idea to apply an adaptive linear neuron (ADALINE) and a multi-layer artificial neural net work (M-LANN) to estimate the fundamental component and the total harmonic content of a distorted signal.
基于多层神经网络和自适应线性组合器的在线谐波跟踪
谐波检测或估计的智能技术目前在电力系统应用中引起了极大的兴趣,它们处理高非线性的能力吸引了研究人员主要基于人工智能即使用人工神经网络来研究这些方法的性能。在文献中提出了许多谐波检测或估计方法,本文重点研究了一种利用自适应线性神经元(ADALINE)和多层人工神经网络(M-LANN)来估计失真信号的基频分量和总谐波含量的新思路。
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