| 风力发电机组故障诊断与状态预测的研究进展 |
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| 引用本文： | 李刚,齐莹,李银强,张建付,张力晖. 风力发电机组故障诊断与状态预测的研究进展[J]. 电力系统自动化, 2021, 45(4): 180-191. DOI: 10.7500/AEPS20200301002 |
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| 作者姓名： | 李刚 齐莹 李银强 张建付 张力晖 |
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| 作者单位： | 华北电力大学控制与计算机工程学院,河北省保定市 071003;复杂能源系统智能计算教育部工程研究中心(华北电力大学),河北省保定市 071003;华北电力大学控制与计算机工程学院,河北省保定市 071003;国网河北省电力有限公司高阳县供电分公司,河北省保定市 071500;华北电力大学电气与电子工程学院,河北省保定市 071003 |
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| 基金项目： | 国家自然科学基金资助项目(51407076)；中央高校基本科研业务费专项资金资助项目(2020MS119)；河北省高等学校科学研究计划资助项目(Z2018210)。 |
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| 摘 要： | 风力发电机组因运行环境恶劣较易发生故障,实现对风电机组适当的状态维护或预防性维护,既能减少故障发生概率、降低维修成本,又能改善电力系统运行的安全性与经济性.首先,通过回顾近年来国内外在风电机组状态维护或预防性维护方面所做的相关研究工作,系统归纳了当前针对风电机组关键部件开展故障诊断和状态预测的难点问题及其研究进展;然后...
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| 关 键 词： | 风力发电机组 故障诊断 状态预测 状态维护 预防性维护 |
| 收稿时间： | 2020-03-01 |
| 修稿时间： | 2020-07-25 |
Research Progress on Fault Diagnosis and State Prediction of Wind Turbine |
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| LI Gang,QI Ying,LI Yinqiang,ZHANG Jianfu,ZHANG Lihui. Research Progress on Fault Diagnosis and State Prediction of Wind Turbine[J]. Automation of Electric Power Systems, 2021, 45(4): 180-191. DOI: 10.7500/AEPS20200301002 |
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| Authors: | LI Gang QI Ying LI Yinqiang ZHANG Jianfu ZHANG Lihui |
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| Affiliation: | 1.School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;2.Engineering Research Center of Intelligent Computing for Complex Energy Systems (North China Electric Power University), Ministry of Education, Baoding 071003, China;3.Gaoyang Power Supply Company of State Grid Hebei Electric Power Co., Ltd., Baoding 071500, China;4.School of Electric and Electronic Engineering, North China Electric Power University, Baoding 071003, China |
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| Abstract: | Wind turbines have a high probability of failure because of the harsh operation environment. Proper condition-based maintenance or preventive maintenance of wind turbines can not only reduce the probability of failure and the maintenance cost, but also improve the safety and economy of power system operation. Firstly, this paper reviews the relevant research on condition-based maintenance or preventive maintenance of wind turbines at home and abroad in recent years, and systematically summarizes the current difficulties and research progress on fault diagnosis and state prediction of key components for wind turbines. Then, the data-driven method of fault diagnosis and state prediction for wind turbines is emphasized. Finally, the future research of fault diagnosis and state prediction for wind turbines is prospected. |
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| Keywords: | wind turbine fault diagnosis state prediction condition-based maintenance preventive maintenance |
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