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Abstract
Ultra-high-frequency (UHF) partial discharge (PD) detection has been proved as a powerful tool to evaluate the status of electrical equipment. Unfortunately, on-site UHF detection suffers from intensive noise interference and great computational burden, which have long been the key bottleneck for its wide utilization in the field environments. In order to provide more valuable information for subsequent processing like PD location, pattern recognition, or severity assessment, a combined preprocessing method for UHF PD detection is proposed in this paper. This scheme integrates a novel Ensemble Empirical Mode Decomposition-based signal reconstruction method and a kurtosis-based pulse window extraction method. To demonstrate the effectiveness of the presented technique, both simulated and laboratory measured PD data are obtained and further tested. Results reveal that this preprocessing method can accurately detect and extract the PD pulse from raw data, which may lessen the computational burden for any further processing. In addition, more accurate and reliable feature information can be retained.