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Fault Diagnosis of an Induction Generator in a Wind Energy Conversion System Using Signal Processing Techniques

机译：利用信号处理技术的风能转换系统中的感应发电机故障诊断

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摘要

In this article, a contribution to fault diagnosis of an induction machine in a wind energy conversion system in closed-loop operation using a combination between short-time Fourier transform and discrete wavelet transform algorithms is proposed. An on-line fault diagnostic technique based on stator currents analysis of the squirrel-cage induction generator is proposed to detect and localize abnormal electrical conditions that indicate, or may lead to, a stator or rotor failure in a squirrel-cage induction generator. This technique also permits identification of a fault severity factor and consequently helps to determine the best choice of corrective maintenance. Furthermore, a generalized model of the squirrel-cage induction generator is used to simulate both the rotor and stator faults, taking iron losses, main flux, and cross-flux saturation into account. The efficiency of diagnostic procedure in closed-loop operation of the wind energy conversion system under non-stationary operating conditions is illustrated with simulation results.
机译：本文提出了一种结合短时傅立叶变换和离散小波变换算法的闭环运行中风能转换系统中的感应电机故障诊断方法。提出了一种基于鼠笼式感应发电机定子电流分析的在线故障诊断技术，以检测和定位指示或可能导致鼠笼式感应发电机定子或转子故障的异常电气状况。该技术还可以确定故障严重性因素，因此有助于确定纠正性维护的最佳选择。此外，使用笼型感应发电机的通用模型来模拟转子和定子故障，同时考虑铁损，主磁通和交叉磁通饱和。仿真结果说明了在非稳态工况下风能转换系统闭环运行中诊断程序的效率。

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