摘要
随着大规模风电场的建设,风电机组的状态监测和故障诊断成为一个重要的研究课题。早期的风电机组状态监测和故障诊断依靠人工巡检,而随着风电机组装机容量的不断增长,人工巡检的成本和难度也随之增加。近年来,基于数据驱动方法的风电机组状态监测和故障诊断逐渐成为热点。文中从运行数据类型出发,对相关研究内容进行综述。首先,针对风电机组数据采集与监控(SCADA)系统,从监测对象角度出发,剖析基于SCADA数据的状态监测与故障诊断方法的研究现状;其次,针对风电机组组件振动数据,分析对比各类振动故障特征提取方法的优点和局限性;然后,针对新兴基于图像数据或数据-图像转换数据的状态监测与故障诊断方法,从单一图像诊断和数据-图像转换评估两方面对现有研究进行论述与总结;最后,对未来状态监测和故障诊断的研究方向进行了展望。
With the construction of large-scale wind farms,the condition monitoring and fault diagnosis of wind turbines have become important research topics.In the early stages,the condition monitoring and fault diagnosis of wind turbines mainly rely on manual inspections.However,as the installed capacity of wind turbines continues to increase,the cost and difficulty of manual inspections have dramatically risen.In recent years,the condition monitoring and fault diagnosis of wind turbines based on datadriven methods have gradually become hot topics.This paper provides a review of related research from the view of operation data types.Firstly,for the supervisory control and data acquisition(SCADA)system of wind turbines,the research status of condition monitoring and fault diagnosis methods based on SCADA data is analyzed from the perspective of monitoring objects.Secondly,based on the vibration data of wind turbine components,the advantages and limitations of various vibration fault feature extraction methods are analyzed and compared.Then,according to the new condition monitoring and fault diagnosis methods based on image data or data-image conversion data,the existing research is discussed and summarized from two aspects of single image diagnosis and data-image conversion evaluation.Finally,the future research directions of condition monitoring and fault diagnosis are prospected.
作者
龙寰
杨婷
徐劭辉
顾伟
LONG Huan;YANG Ting;XU Shaohui;GU Wei(School of Electrical Engineering,Southeast University,Nanjing 210096,China;Extra-high Voltage Branch of State Grid Jiangsu Electric Power Co.,Ltd.,Nanjing 211102,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2023年第23期55-69,共15页
Automation of Electric Power Systems
基金
中国科协青年人才托举工程项目。
关键词
风电机组
数据驱动
数据转换
机器学习
状态监测
故障诊断
wind turbines
data-driven
data conversion
machine learning
condition monitoring
fault diagnosis
作者简介
通信作者:龙寰(1992-),女,博士,副教授,博士生导师,主要研究方向:人工智能及其在电力系统中的应用。Email:hlong@seu.edu.cn;杨婷(1999-),女,硕士研究生,主要研究方向:基于数据驱动的风电场运行维护。E-mail:2457403450@qq.com;徐劭辉(1998-),男,硕士研究生,主要研究方向:基于数据驱动的风电机组故障诊断、特高压直流输电运行维护。Email:645029027@qq.com。
