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A Method Based on Independent Component Analysis for Adaptive Decomposition of Multiple Power Quality Disturbances

机译:基于独立分量分析的多种电能质量扰动自适应分解方法

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This paper proposes a method based on Independent Component Analysis (ICA) to adaptively decompose signals containing multiple power quality disturbances. The method aims to decompose the power system signal (voltage or current) into its isolated disturbances when a multiple disturbance situation occurs, making it possible to obtain more specific information about the different disturbances that can be occurring simultaneously. ICA is originally a multichannel technique. However, the method proposes to use it to blindly separate the different disturbances existing in a single measured signal (single channel). For this purpose, a fixed filter bank is used as a pre-processing step. We demonstrate that the overall response of the proposed method corresponds to an adaptive linear filter bank, and show that as long as the multiple independent disturbances are spectrally disjoint from each other, the method can achieve satisfactory results in their separation. The proposed method is evaluated by means of its application to synthetic, as well as to actual data. A comparison with a method based on the discrete wavelet transform for the same aim shows that the proposed method achieves better results...
机译:本文提出了一种基于独立分量分析(ICA)的方法来自适应地分解包含多个电能质量扰动的信号。该方法旨在在发生多重干扰情况时将电力系统信号(电压或电流)分解为隔离的干扰,从而有可能获得有关可能同时发生的不同干扰的更具体的信息。 ICA最初是一种多通道技术。但是,该方法建议使用它来盲目分离单个测量信号(单通道)中存在的不同干扰。为此,将固定的滤波器组用作预处理步骤。我们证明了该方法的整体响应对应于一个自适应线性滤波器组,并表明只要多个独立的干扰在频谱上彼此不相交,该方法就可以在分离方面取得令人满意的结果。通过将其应用于合成以及实际数据来评估所提出的方法。与基于相同目的的离散小波变换方法的比较表明,该方法取得了较好的效果。

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