FAULT SENSOR DATA RECONSTRUCTION OF 10 MW OFFSHORE WIND TURBINE BASED ON LSTM
To solve the problem of insufficient monitoring data caused by sensor failures in extreme environments,this study proposes an AI-driven offshore wind power monitoring data reconstruction method.In view of the scarcity of offshore wind turbine monitoring data,this study is conducted using numerical simulations results.Based on the aero-hydro-elasto-servo integrated design software Zwind,developed by the authors'team,this study first carries out numerical simulations of a 10MW wind turbine considering all loading cases,and extracts accelerations and inclinations to form the database for a series of data loss situations caused by sensor failures.Then,a data reconstruction model is proposed based on LSTM using acceleration response or inclination response of wind turbine tower,following by its training and verification.Finally,the generalization performance of the LSTM data reconstruction model is investigated on the locations without sensors.The results show that 1)the proposed data reconstruction model can accurately reconstruct the data of fault sensors on wind turbine tower and predict the responses of unmeasured locations using the monitoring data at limited locations;2)the reconstruction results based on inclination response are more accurate than acceleration response.
offshore wind turbinesstructural health monitoringdeep learningsensor failuredata reconstructioninclination response
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