风电机组智能状态评估与故障预测研究进展  

Review on Research Progress of Wind Turbine Intelligent StateAssessment and Fault Prediction

作　　者：李重桂 李录平[1] 刘瑞 杨波 陈茜[1] 邓子豪 LI Zhong-gui;LI Lu-ping;LIU Rui(Changsha University of Science and Technology;Guangzhou Special Pressure Equipment Inspection and Research Institute)

机构地区：[1]长沙理工大学,湖南长沙410014 [2]广州特种承压设备检测研究院

出　　处：《电站系统工程》2020年第4期1-6,11,共7页Power System Engineering

基　　金：广东省质量技术监督局科技项目(2018CT28);广州特种承压设备检测研究院科技项目资助。

摘　　要：针对日趋大型化、复杂化的风电机组,综述了神经网络、模糊逻辑等智能化方法应用于状态评估与故障预测的研究进展。首先介绍了影响风电机组状态的主要因素,并对四种重要组件的常用状态监测技术进行了归纳;其次介绍了风电机组状态评估和故障预测的智能化方法与技术,通过归纳文献资料发现:本领域未来以开发实时在线评估与预测技术发展重点,对SCADA数据的深度挖掘、探索出风电机组状态评价与故障预测新方法将是本领域的研究难点。The application of neural network,fuzzy logic and other intelligent methods in state assessment and fault prediction of the increasingly large and complex wind turbine are reviewed.Firstly,the main factors affecting the state of wind turbine are introduced,and the common state monitoring technologies of four important components in wind turbine are summarized.Secondly,the intellectualized methods and technologies of wind turbine state assessment and fault prediction are introduced in detail.By summarizing the literature,it is found that:this field will focus on the development of real-time online evaluation and prediction technology in the future,and it will be a research difficulty in this field to deeply mine SCADA data and explore new methods for wind turbine state evaluation and fault prediction.

关 键 词：风电机组 状态评估 故障预测 人工智能 

分 类 号：TM315[电气工程—电机]