期刊
IEEE TRANSACTIONS ON SMART GRID
卷 6, 期 2, 页码 980-987出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2014.2386305
关键词
Artificial neural networks (ANN); condition monitoring system (CMS); maintenance management; smart grid; supervisory control and data acquisition systems (SCADAs); wind power generation
资金
- Swedish Energy Agency
- Chalmers University of Technology, Goteborg Energi, Trivents
- SKF, industry
- Swedish Wind Power Technology Centre
Gearbox has proven to be a major contributor toward downtime in wind turbines. The majority of failures in the gearbox originate from the gearbox bearings. An early indication of possible wear and tear in the gearbox bearings may be used for effective predictive maintenance, thereby reducing the overall cost of maintenance. This paper introduces a self-evolving maintenance scheduler framework for maintenance management of wind turbines. Furthermore, an artificial neural network (ANN)-based condition monitoring approach using data from supervisory control and data acquisition system is proposed. The ANN-based condition monitoring approach is applied to gearbox bearings with real data from onshore wind turbines, rated 2 MW, and located in the south of Sweden. The results demonstrate that the proposed ANN-based condition monitoring approach is capable of indicating severe damage in the components being monitored in advance.
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