基于电流特征分析的鼠笼式风力发电机故障检测方法

J. Royo, F. Arcega
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引用次数: 14

摘要

目前,可再生能源发电系统正在增加其存在。本文研究了机器电流特征分析(MCSA)如何可靠地诊断鼠笼发电机的故障。本文重点研究了适合于鼠笼型风力发电系统的早期故障检测和故障检测方法。该系统对异步发电机转子断条、定子绕组短路和轴承故障三种类型的故障进行诊断。在对电流数据进行处理后,应用经典的快速傅立叶变换检测MCSA在健康状态和各种故障状态下的特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Current Signature Analysis as a Way for Fault Detection in Squirrel Cage Wind Generators
At the moment renewable generation systems are increasing its presence. The paper is about how machine current signature analysis (MCSA) can reliably diagnose faults in squirrel cage generators. This paper focuses on the experimental investigation for incipient fault detection and fault detection methods, suitably adapted for use in wind generator systems using squirrel cage. The proposed system diagnoses asynchronous generators having three types of faults such as broken rotor bars, short circuit of stator windings and bearing fault. After processing current data the classical fast Fourier transform is applied to detect characteristics under the healthy and various faulted conditions with MCSA.
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