Abstract
To facilitate continuous development of the wind power industry, maintaining technological innovation and reducing cost per kilowatt hour of the electricity generated by the wind turbine generator system (WTGS) are effective measures to facilitate the industrial development. Therefore, the improvement of the system availability for wind farms becomes an important issue which can significantly reduce the operational cost. To improve the system availability, it is necessary to diagnose the system fault for the wind turbine generator so as to find the key factors that influence the system performance and further reduce the maintenance cost. In this paper, a wind farm with 200 MW installed capacity in eastern coastal plain in China is chosen as the research object. A prediction model of wind farm’s faults is constructed based on the Gaussian process metamodel. By comparing with actual observation results, the constructed model is proved able to predict failure events of the wind turbine generator accurately. The developed model is further used to analyze the key factors that influence the system failure. These are conducive to increase the running and maintenance efficiency in wind farms, shorten downtime caused by failure, and increase earnings of wind farms.
Highlights
Nowadays, global climate warming has caused frequent occurrences of climatic anomaly, extreme climate, and major natural disasters, bringing serious challenges to sustainable development
Wind power generation is becoming a mature technology with cost competitiveness [1,2,3,4,5,6]. e Renewables 2019 Global Status
To help wind power industrial development cope with the challenge of FIT reduction successfully, optimizing management of the wind farms is another effective strategy except for reducing cost per kilowatt hour of the electricity generated by wind turbine generator system (WTGS) by technological innovation
Summary
Global climate warming has caused frequent occurrences of climatic anomaly, extreme climate, and major natural disasters, bringing serious challenges to sustainable development. To help wind power industrial development cope with the challenge of FIT reduction successfully, optimizing management of the wind farms is another effective strategy except for reducing cost per kilowatt hour of the electricity generated by WTGS by technological innovation. Ese faults have to be predicted and diagnosed accurately by scientific methods, enabling to formulate targeted strategies It is found from actual maintenance practices of wind turbine generators that there is a high nonlinear relationship between the turbine fault and relevant factors. The Gaussian process metamodel is constructed for prediction of the wind turbine generator fault. (ii) Develop Gaussian process metamodels for different fault systems to represent the relationship between fault factors and system failure time.
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