风力发电机组故障诊断与预测技术研究综述

发布时间：2018-02-11 16:31

本文关键词： 风力发电机组故障诊断故障预测数据采集与监控系统　出处：《仪器仪表学报》2017年05期 　论文类型：期刊论文

【摘要】：随着风力发电机组装机容量的快速发展,累计运行时间的持续增长,风电机组的维护问题日益突出,迫切需要研发有效的风电机组故障诊断与预测系统。从故障诊断和故障预测两个方面,归纳风力发电机组的主要故障特点;针对故障诊断难点问题,分析和总结基于振动、电气信号分析和模式识别算法的故障诊断方法的研究现状,指出各种方法的技术特点、局限性和今后的发展趋势;针对风电机组中机械结构和电子系统性能退化的各自特点,归纳当前的研究进展,提出物理失效模型和数据驱动模型融合的故障预测方法;最后,归纳了利用风力发电机组数据采集与监控系统(SCADA)数据进行故障诊断与预测的最新进展及需要进一步研究的问题。
[Abstract]:With the rapid development of wind turbine installed capacity and the continuous increase of cumulative operating time, the maintenance problem of wind turbine is becoming increasingly prominent. It is urgent to develop an effective wind turbine fault diagnosis and prediction system. The main fault characteristics of wind turbine are summarized from two aspects: fault diagnosis and fault prediction. The present research status of fault diagnosis methods of electrical signal analysis and pattern recognition algorithm, pointing out the technical characteristics, limitations and future development trends of various methods, aiming at the respective characteristics of the degradation of mechanical structure and electronic system performance in wind turbine, The current research progress is summarized, and a fault prediction method based on the fusion of physical failure model and data-driven model is proposed. Finally, This paper summarizes the latest progress in fault diagnosis and prediction by using data acquisition and monitoring system (SCADA) of wind turbine and the problems that need to be further studied.
【作者单位】：浙江工业大学特种装备制造与先进加工技术教育部重点实验室;浙江工业大学机械工程学院;浙江工业大学海洋研究院;浙江运达风电股份有限公司;风力发电系统国家重点实验室;
【基金】：国家自然科学基金(51505424,51675484) 浙江省自然科学基金(LY15E050019)项目资助
【分类号】：TM315

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