Wind turbine fault detection and diagnosis using LSTM neural network

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.