Residual diagnosis model based on wavelet neutral network and its application to hydroelectric generator unit

Abstract

Most of current diagnosis methods of hydroelectric generator unit (HGU) performed not well when lacking domain expert knowledge, in order to address this problem, we propose a novel residual diagnosis model based on wavelet neural network (RDM-WNN) and weighed fuzzy set theory for quantitative diagnosis of HGU in this paper. First, the main working condition parameters (MWCP) are extracted according to the mutual information between the performance parameters and working condition parameters, and used as input feature vector to construct the RDM-WNN model. Second, relative residual are calculated by comparing the output vector of RDM-WNN model to the corresponding real values. Third, the relative residual values are used to implement quantitative diagnosis of HGU using weighted fuzzy set theory. The proposed method was verified on a real HGU with 100 normal working conditions, 200 slight faults working conditions, and 200 fully faults working conditions. Six groups of partial load experiments were implemented. The results demonstrate that the proposed method is an effective means for fault diagnosis of HGU.