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Prediction of Gearbox Oil Temperature of Wind Turbine Based on GRNN-LSTM Combined Model

The gearbox oil temperature of offshore wind turbines(WTS) is one of the important indexes of gearbox state monitoring and fault warning. Accurately predicting the change trend of gearbox oil temperature can improve the reliability of the unit and reduce operation and maintenance costs. In order to improve the accuracy of oil temperature prediction, This paper proposes a prediction model based on long short-term memory(LSTM)- generalized regression neural network(GRNN) weighted combination. From the perspective of the short-term, mid-term and long-term prediction performance of the model, this article first compares the model with GRNN and LSTM two single models, and then compares with other combined models. Through comparison, it is found that the combined model can effectively combine the good mid- and long-term prediction ability of the LSTM algorithm with the good short-term prediction ability of the GRNN algorithm, which greatly improves the accuracy and stability of oil temperature prediction.