Characteristic parameter degradation prediction of hydropower unit based on radial basis function surface and empirical mode decomposition

Abstract

A prediction method of characteristic parameter degradation for a hydropower unit is presented based on radial basis function (RBF) interpolation, empirical mode decomposition (EMD), approximate entropy, artificial neural network and grey theory. Considering the effect of active power and working head, the characteristic parameter degradation model of a hydropower unit is built by using RBF interpolation. The EMD method is used to decompose the characteristic parameter degradation time series of the hydropower unit into a number of intrinsic mode function (IMF) components. The approximate entropy of each IMF component is calculated. According to their different properties, the neural network or grey theory is used to predict them, respectively. All the predicted results are added to obtain the final forecasting result of the original characteristic parameter degradation time series. The case study results demonstrate that the proposed method has an extremely high prediction accuracy, and can be applied in the hydropower unit condition prediction effectively.