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Fault diagnosis of hydropower generator based on LSTM- weight

Traditional detection models are not sensitive to small abnormal changes. In order to improve the sensitivity of the model, in this study, the weight factor is introduced into the traditional LSTM detection model. By using the correction mechanism, the LSTM-weight model makes the prediction model not deviate from the normal track following the appearance of outliers, which ensures the model prediction has good stability. And after a small change in the data, the LSTM-weight model can detect this small change. Validated on a Brazilian hydropower dataset, the results show that the LSTM-weight model can detect small changes that the traditional detection model cannot detect, which shows the effectiveness of the proposed method.