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基于小波变换的信号奇异性指数计算方法及其应用
引用本文:何正友,钱清泉.基于小波变换的信号奇异性指数计算方法及其应用[J].电力自动化设备,2000,20(3):12-15.
作者姓名:何正友  钱清泉
作者单位:西南交通大学,电气自动化所,四川,成都,610031
摘    要:电力设备故障时将产生具有奇异性的非平稳信号,小波变换在时域和频域内同时具有局部化能力,是分析故障信号奇异性的有利工具,为电力设备故障检测提供了新思路,首先讨论了信号奇异性的小波变换物,在此基础上,研究了信号全局奇异性指数和局部奇异性指数(Lipschitz指数)的计算方法。仿真分析了电流基波及各次谐波等理论信号等的奇展性指数特点,将其应用于电力设备故障检测中,得到了较好的结果。

关 键 词:小波变换  奇异性  Lipschitz指数  电力设备

A Computational Approach to Signal Singularity Exponent Based on Wavelet Transform and Its Application
HE Zheng you,QIAN Qing quan.A Computational Approach to Signal Singularity Exponent Based on Wavelet Transform and Its Application[J].Electric Power Automation Equipment,2000,20(3):12-15.
Authors:HE Zheng you  QIAN Qing quan
Abstract:Non stationary signal with singularities is presented when electrical devices are faulting.Wavelet transform possesses time frequency localization ability, so it provides a powerful tool to analyze fault signal and a new idea to detect fault. Signal singularities based on wavelet transform are introduced. According to this,a computational approach to it is studied. The singularity exponents (Lipschitz exponents) of current and harmonics are analyzed, simulation result demonstrates that the global and partial singularity exponent is suitable for electrical device fault detection.
Keywords:wavelet transform  singularity  Lipschitz exponent  fault detection
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