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Research on Opportunity Maintenance Strategy of Wind Turbines Based on Incomplete Maintenance
Author(s) -
C. Liu,
R. Y. Pang,
Chaoshuai Han,
T. T. Liu
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1043/2/022051
Subject(s) - reliability engineering , wind power , turbine , component (thermodynamics) , reliability (semiconductor) , optimal maintenance , weibull distribution , interval (graph theory) , predictive maintenance , maintenance engineering , preventive maintenance , maintenance actions , engineering , computer science , power (physics) , electrical engineering , mechanical engineering , statistics , physics , mathematics , quantum mechanics , combinatorics , thermodynamics
Aiming at the problem of single maintenance method and high cost of wind turbines, an opportunity maintenance strategy for wind turbines based on incomplete maintenance is proposed. Firstly, based on the historical fault data of the equipment operation, the reliability of each component of the wind turbine is modeled using Weibull distribution, and the service life regression factor and failure rate increasing factor is introduced to build the incomplete maintenance strategy model of the wind turbine. Secondly, according to the opportunity maintenance theory and the reliability requirements of wind turbine components, determine the preventive maintenance reliability threshold and the opportunity maintenance reliability threshold of each component, and adopt three types of maintenance methods: incomplete repair, opportunity repair and replacement of key components within the opportunity repair interval. Based on this, a multi-component opportunity maintenance model for wind turbines based on incomplete maintenance and its decision process are constructed. Finally, the model was simulated and verified through specific wind turbine maintenance cases, and the model was solved using genetic algorithms to determine the optimal opportunity maintenance strategy. Simulation results show that the strategy can coordinate the maintenance time of various components, realize the simultaneous maintenance of multiple components, improve maintenance efficiency, and reduce maintenance costs, thereby verifying the effectiveness of the proposed strategy.

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