Characterizing the diverter switch of a load tap changer in a transformer using wavelet and modal analysis

Feature extraction of On-Load Tap-Changer Based on Improved EMD Energy Spectrum

“…By extracting certain feature from the vibration signal, it can diagnose the status of OLTC, timely repair and improve the reliability of the equipment. Scholars Rivas et al used hilbert transform and discrete wavelet transform to analyze the vibration signals of different parts of OLTC, and obtained the peak time and amplitude information of the signal envelope [1] . Scholars Zhao Tong and others reconstructed the vibration signal in high-dimensional space, and defined the phase point spatial distribution coefficient to identify the normal and fault states of OLTC [7] [8] .Ma Hongzhong and Zhang Huifeng of Hohai University proposed the diagnosis using EMD-HT time-frequency analysis algorithm, EMD entropy and wavelet entropy [2] [3] .These methods solve the OLTC feature extraction problem to a certain extent, but many methods are limited to feature extraction for a few hundred milliseconds of the in-position section, ignoring the feature information contained in the stationary section.…”