Reconstruction of integrated hydraulic turbine characteristics curve based on classification weight neural network

Abstract

To address the shortcomings in the drawing and processing of comprehensive characteristic curves of hydraulic turbines and the problems in the operation and management of small hydropower stations, this paper proposes a method for reconstructing the comprehensive characteristic curves based on classification weight neural network. Firstly, this study discretizes the comprehensive characteristic curve of the target turbine factory package to obtain multiple sets of discrete data points, and compares them with the actual operating data of small hydropower plants, and then pre-processes the operating data by the type and operating characteristics of the unit. Next, this paper uses XGBoost to classify the two types of data to obtain the leaf node optimal scores and uses the scores as the prediction weights of the deep neural network to grid the discrete points of the hydraulic turbine for prediction, and finally obtains the specific integrated characteristic graph of the unit. The method based on classification weight neural network can substantially improve the accuracy and reliability of composite characteristic curves while avoiding multivariate nonlinear mathematical problems, which provides a new method and idea for the reconstruction and expansion of the hydraulic turbine characteristic curve, and also provides a theoretical basis for the safe and economic operation of hydropower units.