Research on Diagnosis Method for State of Transformer Winding of Trainset Based on Frequency Response and Image Feature Extraction
The trainset transformer is the core equipment to ensure the stable operation of high-speed railroads,while the frequency response method is an effective method to detect the state of the transformer windings.In order to improve the accuracy of the diagnosis of on-board transformer winding state,combining the transient signals with the frequency re-sponse method,this paper proposed a trainset transformer winding state diagnosis method based on the frequency re-sponse and image feature extraction.Firstly,a simulation platform was built for testing on-board transformer winding faults to obtain the frequency response curves of different fault types and fault locations,and to combine the amplitude-frequency and phase-frequency curve information by using the Gram-like matrix set.Secondly,the density hierarchy method was converted to a pseudo-color image to extract the corresponding grayscale covariance matrix and grayscale difference matrix eigenvalues,and to finally diagnose the winding faults according to the pelican-optimized support vector machine method.The experimental validation results show that in the case of on-board transformer winding fault,the pseudo-color image can reflect the fault information,which is conducive to image analysis and feature extraction.The di-agnosis method for the state of trainset transformer winding based on frequency response and image feature extraction can identify the typical types and locations of the on-board transformer winding faults.
万方数据
维普
