一种无刷同步发电机旋转整流器故障快速识别方法

摘要

Focusing on the slow speed problem of existing brushless synchronous generator rotating rectifier fault recognition methods,this paper presents a fast recognition technique based on improved extreme learning machine (ELM).The chicken swarm optimization (CSO) is used to optimize the parameters of ELM,and hence,an optimized model of ELM can be achieved,and then applied it to rotating rectifier faults recognition of brushless synchronous generator.Experimental results show that,the optimized ELM can achieve good diagnosis performance and high classification speed.The presented method can be considered to the application of brushless synchronous generator rotating rectifier faults recognition and localization.%针对目前无刷同步发电机旋转整流器故障识别方法识别速度慢的问题,提出一种基于改进极限学习机的故障快速识别技术.该方法通过鸡群算法优化极限学习机的参数,得到优化的识别模型,并将其应用于无刷同步发电杌旋转整流器的故障识别中.实验结果表明,经过优化的极限学习机与现有分类方法相比,具有很好的诊断性能和较高的分类速度.该方法适用于无刷同步发电机旋转整流器故障快速识别和定位.