Abstract
The increasing demand for wind power requires effective and reliable fault detection and diagnosis for wind turbines, which would reduce down-times and moderate repair costs. By adopting the Long Short Term Memory (LSTM) networks, we accurately predict the time-series data of proper functioning wind turbines based on the measured data. Compared with the traditional fault detection algorithm, our method could detect the faults more effectively. Simulation results verified that the proposed method could accurately and speedily detect the possible sensor faults and system faults defined in the benchmark model of wind turbines.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
