Classification of simultaneous multiple partial discharge sources based on probabilistic interpretation using a two-step logistic regression algorithm

In online condition assessment monitoring of high voltage (HV) insulators, it is often required to identify multiple, simultaneously activated partial discharge (PD) sources that happen in the insulation of the HV apparatus. Phased resolved partial discharge (PRPD) patterns are commonly used to identify PD sources. However, multiple, concurrent PD sources sometimes result in partially overlapped patterns, which make them hard to be identified. In this paper, we develop an accurate, reliable algorithm by constructing a novel two-step logistic regression (LR) model to conduct probabilistic identification of multi-source PDs. To this end, principal component analysis is applied on a database to construct a low dimensional space associated with single-source PDs. Samples of multi-source PDs are then projected onto this space and one-class kernel support vector machine is adopted to distinguish multi-source PDs from single-source ones. Finally, classification is performed by estimating the probability (degree of membership) of each PRPD pattern arising from different multi-source PDs following two rounds of LR modeling. To evaluate the performance of our proposed method, we study a number of multi-source PD models to simulate common defects of Gas-Insulated Switchgear (GIS) in small-scale laboratory test cells with realistic SF
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gas condition. Observations are obtained using fingerprints generated by a novel approach from recorded PRPD patterns. Comprehensive performance evaluation of the proposed algorithm and its advantages are conducted and the development of analytical equations is presented. The results of this paper can be used to design a solid basis for an automated multi-source classification system, which facilitates multi-source PD identification in early stages and safe operation of HV apparatus.