Degradation Trend Assessment of Hydropower Units Based on DAE-GBDT
To accurately assess the operational status of hydropower units and ensure their safe operation,this paper proposes a method for evaluating the degradation degree of hydropower units based on a health model using Deep Autoencoder(DAE)and Gradient Boosting Decision Trees(GBDT).The deep autoencoder(DAE)is utilized to compress and refine critical information from operating parameters.Subsequently,a health model is established using gradient boosting decision trees(GBDT)to learn the potential relationship between vibration values and operating parameters.Finally,the degradation condition of the unit is obtained based on the constructed health model and operational data.The proposed model is validated using a case study of a pumped storage unit,demonstrating higher accuracy and the ability to generate reliable degradation trends.
万方数据
维普
