A hybrid algorithm based on s transform and affinity propagation clustering for separation of two simultaneously artificial partial discharge sources


This paper presents a hybrid algorithm for separation of two simultaneous partial discharge (PD) sources of oil-paper insulation primarily based on S rework (ST) and affinity propagation clustering (APC). Similarities between PD pulses are acquired by comparisons of the corresponding ST-amplitude (STA) matrices, that are input of APC to realize the PD pulses separation and get 2 sub-groups of PD pulses having similar time-frequency characteristics. A classification-based model for separation results validation are developed using a support vector machine with particle swarm optimization (PSO-SVM) classifier and twenty seven part-resolved partial discharge (PRPD) statistical features. Artificial defect models are made to simulate two PD sources simultaneously active. Several PD knowledge of various two simultaneous PD sources are acquired in laboratory and adopted for algorithm testing. It is shown that ST computes terribly quick and is suitable for on-line PD applications. The separation results of PD data produced in laboratory are verified by the developed validation model, which demonstrate that ST combined with APC will effectively eliminate pulse-formed noises (PSN) and separate pulses of two simultaneous PD sources. Comparisons with typical separation ways from the cutting-edge offer better separation performance of the proposed ST combined with APC algorithm for two simultaneous PD sources. The obtained leads to this work provide a solid basis for the Data Mining technique that can be used to facilitate PD diagnosis of transformers.

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