Density-based clustering methods for unsupervised separation of partial discharge sources. (May 2019) - Castro Heredia, Luis Carlos; Rodrigo Mor, Armando
Highlights: PD measuring circuit affects PD features and in turn effectiveness of clustering techniques. Density Peak Clustering method may fail with data sets having large variation of density and scatter. Density Peak Clustering method and smoothed density methods are combined to better separate clusters. The proposed methodology splits data sets in subsets improving computational performance. Abstract: The recognition of partial discharge (PD) sources is an important task of the monitoring and diagnostics of high-voltage components. Nowadays, digital PD measuring systems have the capability of extracting features and form scatter plots with such data sets. Part of an unsupervised PD analysis system is to discover clusters within the data sets and link them to particular PD sources. Due to the nature of PD data sets, clusters may appear very close to each other or even merged hindering the separation of sources. Clustering methods based on spatial density such as the density peak clustering (DPC) method and DBSCAN are suitable approaches to discover clusters within PD data sets. However, their accuracy can be reduced due to the proximity among clusters. In this paper, a new method is presented to improve the accuracy of the DPC method. Our method proposes to partition the data set and later pass the resulting subsets to the DPC method. The partitioning is based on the spatial density of data computed by a smoothed density method (SD). SD has the advantage of being fast and not requiring high computational power. As a final step, a routine is applied to group the sub clusters as per the DPC method having a threshold for the data contour distance as a criterion. This method proved higher accuracy to discover clusters in actual PD data sets. However, the threshold for the data contour distance still needs further research.