基于人工智能算法的风电机组状态监测和故障诊断技术研究综述
Review of Artificial Intelligence Algorithms-based Wind Turbine Condition Monitoring and Fault Diagnosis Techniques
王中行 1周元贵 2张学广1
作者信息
- 1. 哈尔滨工业大学电气工程及自动化学院,黑龙江 哈尔滨 150000
- 2. 哈尔滨工业大学电气工程及自动化学院,黑龙江 哈尔滨 150000;中国大唐集团科学技术研究总院有限公司西北电力试验研究院,陕西 西安 710000
- 折叠
摘要
随着我国风电产业高速发展,风电机组服役时间延长,故障率和运维成本随之增加.利用人工智能算法对风电大数据进行数据挖掘,实现风电机组的状态监测与故障诊断,对风电产业提质增效具有重要的现实意义,近年来逐渐成为研究热点.文中介绍了风电机组数据采集与监控(Supervisory Control and Data Acquisition,SCADA)系统和振动信号数据的特性,阐述了风电机组状态监测和故障诊断智能算法的框架,归纳总结了相关研究成果,并对风电机组状态监测和故障诊断技术所面临的挑战和发展趋势进行了展望.
Abstract
With the rapid development of my country's wind power industry,the service life of wind turbines has been extended,and the failure rate and maintenance costs have increased accordingly.Using artificial intelligence algorithms to mine wind power big data and achieve condition monitoring and fault diagnosis of wind turbines has important practical significance for improving the quality and efficiency of the wind power industry,and has gradually become a research hotspot in recent years.This article introduces the characteristics of the wind turbine supervisory control and data acquisition system and vibration signal data,and explains the framework of the intelligent algorithm for wind turbine condition monitoring and fault diagnosis.Relevant research results are summarized,and the challenges and development trends faced by wind turbine condition monitoring and fault diagnosis technology are prospected.
关键词
风电机组/数据驱动/深度学习/状态监测/故障诊断Key words
wind turbine/data-driven/deep learning/condition monitoring/fault diagnosis引用本文复制引用
基金项目
国家自然科学基金面上项目(51977046)
出版年
2024
NETL
NSTL
万方数据