Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research

被引:16
作者
Peng, Han [1 ]
Li, Songyin [1 ]
Shangguan, Linjian [1 ]
Fan, Yisa [1 ]
Zhang, Hai [1 ]
机构
[1] North China Univ Water Resources & Elect Power, Sch Mech Engn, Zhengzhou 450045, Peoples R China
关键词
wind energy industry; equipment failure; operation and maintenance cost; intelligent operation and maintenance technology; LIFE-CYCLE ASSESSMENT; FAULT-DIAGNOSIS; TUNNEL EXPERIMENTS; POWER-GENERATION; ENERGY; MODEL; COSTS; OPTIMIZATION; EXTRACTION; EXTENSION;
D O I
10.3390/su15108333
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Power generation from wind farms is growing rapidly around the world. In the past decade, wind energy has played an important role in contributing to sustainable development. However, wind turbines are extremely susceptible to component damage under complex environments and over long-term operational cycles, which directly affects their maintenance, reliability, and operating costs. It is crucial to realize efficient early warning of wind turbine failure to avoid equipment breakdown, to prolong the service life of wind turbines, and to maximize the revenue and efficiency of wind power projects. For this purpose, wind turbines are used as the research object. Firstly, this paper outlines the main components and failure mechanisms of wind turbines and analyzes the causes of equipment failure. Secondly, a brief analysis of the cost of wind power projects based on equipment failure is presented. Thirdly, the current key technologies for intelligent operation and maintenance (O&M) in the wind power industry are discussed, and the key research on decision support systems, fault diagnosis models, and life-cycle costs is presented. Finally, current challenges and future development directions are summarized.
引用
收藏
页数:35
相关论文
共 204 条
  • [1] Review of Flow-Control Devices for Wind-Turbine Performance Enhancement
    Akhter, Md Zishan
    Omar, Farag Khalifa
    [J]. ENERGIES, 2021, 14 (05)
  • [2] A Joint Optimization Model for Transmission Capacity and Wind Power Investment Considering System Security
    Alshamrani, Ahmad M.
    El-Meligy, Mohammed A.
    Sharaf, Mohamed Abdel Fattah
    Nasr, Emad Abouel
    [J]. IEEE ACCESS, 2023, 11 : 15578 - 15587
  • [3] A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer
    Altan, Aytac
    Karasu, Seckin
    Zio, Enrico
    [J]. APPLIED SOFT COMPUTING, 2021, 100
  • [4] An IoT-Based Life Cycle Assessment Platform of Wind Turbines
    An, Jinjing
    Zou, Zhuo
    Chen, Guoping
    Sun, Yaojie
    Liu, Ran
    Zheng, Lirong
    [J]. SENSORS, 2021, 21 (04) : 1 - 21
  • [5] Annoni J, 2018, P AMER CONTR CONF, P6200, DOI 10.23919/ACC.2018.8430751
  • [6] [Anonymous], 2017, EUR OFFSH WIND IND K
  • [7] [Anonymous], 2014, HALF YEAR REP 2014
  • [8] [Anonymous], 2016, WIND POW 2015 EUR ST
  • [9] Maintenance management based on Machine Learning and nonlinear features in wind turbines
    Arcos Jimenez, Alfredo
    Zhang, Long
    Gomez Munoz, Carlos Quiterio
    Garcia Marquez, Fausto Pedro
    [J]. RENEWABLE ENERGY, 2020, 146 : 316 - 328
  • [10] Wind Turbine Yaw Control Optimization and Its Impact on Performance
    Astolfi, Davide
    Castellani, Francesco
    Natili, Francesco
    [J]. MACHINES, 2019, 7 (02)