Condition parameter degradation assessment and prediction for hydropower units using Shepard surface and ITD

Abstract

A condition parameter degradation assessment and prediction model was developed to evaluate and forecast hydropower units based on the Shepard surface, intrinsic time-scale decomposition (ITD), a radial basis function (RBF) artificial neural network and grey theory. The model includes the effect of the active power and the working head on the hydropower unit’s condition. The condition parameter degradation time series is decomposed into a finite number of proper rotation components and an approximate component using the ITD method. The GM(1,1) model (a first-order one-variable grey model) is then used to predict the approximate component time series. The proper rotation component time series are then predicted separately by building different RBF neural networks. Finally, the original condition parameter degradation time series is found by adding these results. Real condition monitoring data from a pumped storage power station in China is used to verify the method. The results show that this method accurately reflects the condition parameter degradation for the hydropower unit.