Condition monitoring and fault diagnosis of hydropower generator based on LSTM correction model

Abstract

Hydropower station is vital for the stable growth of the national economy. How to timely warn the possible faults of hydropower stations has become an increasingly popular research topic. The traditional detection model is difficult to detect the small abnormal changes in the data, and these abnormal changes are often the precursor of faults. To improve the sensitivity of the traditional detection model, this study introduced a weight factor into the traditional LSTM detection model. By using the correction mechanism, the LSTM correction model makes the prediction model never deviate from the normal track following the appearance of abnormal data. This ensures that the model can generate large residuals after abnormal data occur so that we can detect these abnormal data in time. Finally, this paper puts forward two factors related to equipment health and integrates these two factors to form a health index. The results show that the LSTM correction model based on the health index can not only detect small changes that cannot be detected by traditional detection models but also knows the wear and tear of equipment during operation based on the changes in health indicators.