Partial Discharge Detection Framework Employing Spectral Analysis of Horizontal Visibility Graph

In this article, we propose a novel method for identification of partial discharge (PD) signals employing horizontal visibility graph spectral analysis (HVGSA). Horizontal visibility graph (HVG) converts a time series into an undirected graphical network while preserving its temporal characteristics. In the present contribution, PD signals of single and multiple void discharges were measured using a high frequency current transformer (HFCT) sensor and subsequently transformed to undirected graphical networks using HVG. From the HVG of the PD signals, several spectral graph features were extracted for recognition of PD signals. The extracted features were further subjected to analysis of variance (ANOVA) test to examine their statistical significance and followed by FDR correction to select the most discriminative features. The PD signals were classified using three machine learning classifiers. We also investigated the performance of our method by varying penetrable distance- an important parameter of HVG for robust detection of PD signals in presence of noise. Investigations revealed that very high recognition accuracy has been obtained in discriminating different PD signals using HVGSA. Interestingly, we also observed that in the presence of noise, detection accuracy can be improved significantly by increasing the penetrable distance within a permissible range. Finally, an optimum value of penetrable distance parameter has been determined for accurate recognition of PD signals at different noise levels. The proposed technique can be applied for real-life PD signal detection for insulation condition monitoring.