Signal analysis and feature generation for pattern identi cation of partial discharges in high-voltage equipment

This paper proposes a method for the identi cation of di erent partial discharges (PD) sources through the analysis of a collection of PD signals acquired with a PD measurement system. This method, robust and sensitive enough to cope with noisy data and external interferences, combines the characterization of each signal from the collection, with a clustering procedure, the CLARA algorithm. Several features are proposed for the characterization of the signals, being the wavelet variances, the frequency estimated with the Prony method, and the energy, the most relevant for the performance of the clustering procedure. The result of the unsupervised classi cation is a set of clusters each containing those signals which are more similar to each other than to those in other clusters. The analysis of the classi cation results permits both the identi cation of di erent PD sources and the discrimination between original PD signals, re ections, noise and external interferences. Email address: [email protected] (O. Perpiñán) Preprint submitted to Electric Power Systems Research August 21, 2012 The methods and graphical tools detailed in this paper have been coded and published as a contributed package of the R environment under a GNU/GPL licence.