Denoising technique for partial discharge signal : A comparison performance between artificial neural network, fast fourier transform and discrete wavelet transform

N. M. Yusoff, M. Isa, H. Hamid, M. Adzman, M. Rohani, C. Yii, N. N. Ayop
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引用次数: 17

Abstract

This paper presents de-noising of PD signal using three different techniques; ANN, FFT and DWT. The objective of this paper is to yield the PD signal from the disturb signal which is the combination of PD and noise signal. These signals are generated using EMTP-ATP simulation environment. This research used the straightforward procedure in the de-noising technique. The accuracy of the de-noising is based on the calculation of SNR. The result of this research shows ANN is the best de-noising technique as the calculated SNR is the highest with 0.635938, followed by FFT technique with SNR of 0.452903 and lowest SNR is DWT with −0.154054.
局部放电信号去噪技术:人工神经网络、快速傅立叶变换和离散小波变换的性能比较
本文介绍了三种不同的PD信号降噪技术;人工神经网络,FFT和DWT。本文的目标是从干扰信号中得到PD信号,该干扰信号是PD信号和噪声信号的组合。这些信号是利用EMTP-ATP仿真环境产生的。本研究采用了简单的降噪方法。去噪的精度取决于信噪比的计算。研究结果表明,人工神经网络降噪效果最好,计算得到的信噪比最高,为0.635938;FFT降噪效果次之,信噪比为0.452903;DWT降噪效果最低,为- 0.154054。
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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