High-resolution spectral analysis method to identify rotor faults in WRIM using Neutral Voltage

K. Dahi, Soumia Elhani, Said Guedira, Lahbib Sadiki, I. Ouachtouk
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引用次数: 2

Abstract

In this paper, we perform an on-line fault diagnosis using high-resolution spectral analysis of the voltage between neutrals to detect rotor faults in Wound Rotor Induction Machine. For that, a Multiple Signal Classification (MUSIC) technique has been introduced in the IM fault diagnosis world. MUSIC has the highest frequency resolution among all FFT-based methods, non parametric and parametric methods. Despite its effectiveness in improving the freqency resolution, MUSIC is computationally too complex to implement in real time and it takes a long computation time to find more frequencies by increasing the order of the frequency signal dimension. To address this problem, a zoom MUSIC algorithm (ZMUSIC) that combined the zoom technique and the MUSIC algorithm was proposed. We present the results obtained for real data to verify the proposed method for monitoring of Wound Rotor Induction Machine.
基于中性点电压的wrm转子故障高分辨率频谱分析方法
本文利用中性点间电压的高分辨率频谱分析对绕线转子感应电机的转子故障进行在线诊断。为此,多信号分类(MUSIC)技术被引入到IM故障诊断领域。MUSIC在所有基于fft的方法、非参数方法和参数方法中具有最高的频率分辨率。尽管MUSIC在提高频率分辨率方面是有效的,但由于计算太复杂,无法实时实现,并且通过增加频率信号维数的顺序来找到更多的频率需要很长的计算时间。针对这一问题,提出了一种结合缩放技术和MUSIC算法的缩放MUSIC算法(ZMUSIC)。给出了实际数据的结果,验证了所提出的绕线转子感应电机的监测方法。
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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