Recognition for Operation States of Hydroelectric Generating Units Based on Continuous Wavelet Transform and Deep Separable Convolution
To determine the operation states of hydroelectric units quickly and accurately,a recognition method based on Continuous Wavelet Transform(CWT)and Deep Separable Convolution(DSC)was proposed.The acquisition of vi-bration signals under various operation conditions of units were analyze by CWT,and its multiscale time-frequency distri-bution information was obtained.In order to convert time-frequency information into digital image format,a series of processes including data normalization,geometric size transformation and format conversion were performed to process the time-frequency information.Finally,a Deep Separable Convolution Neural Network(DSCNN)model was established to train the model according to digital image information.Operation states under different power and transient conditions can be identified effectively.Based on the vibration signals collected from a Kaplan hydroelectric unit of a hydropower sta-tion located in Southwest China,the identification of various operating conditions of the unit has been achieved with an ac-curacy rate of 98.06%.
万方数据
维普
