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

This paper proposes a method for the identification of different partial discharges (PDs) 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 classification 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 classification results permits both the identification of different PD sources and the discrimination between original PD signals, reflections, noise and external interferences. 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 license.

Gas discharges for continental Spain: geochemical and isotopic features

In this work the results of a geochemical and isotopic survey of 37 gas discharges was carried out in continental Spain are presented and discussed. On the basis of the gas chemical composition, four different areas can be distinguished, as follows: 1) Selva-Emborda (SE) region; 2) Guadalentin Valley (GV); 3) Campo de Calatrava (CC) and 3) the inner part of Spain (IS). The SE, GV and CC areas are characterized by CO2-rich gases, while IS has N2 as main gas compound. The CO2-rich gases can be distinguished at their turn on the basis on the helium and carbon isotopic composition. The SE and CC areas have a strong mantle signature (up to 3 Ra). Nevertheless, the carbon isotopic composition of CC is within the mantle range and that of SE is slightly more negative (down to -8‰ PDB). The GV gases have a lower mantle signature (61 Ra) with respect to SE and CC and more negative carbon isotopes (6-10‰ PDB). It is worth to mention that the SE, GV and CC areas are related to the youngest volcanic activity in continental Spain, for example the Garrotxa Volcanic Field in Catalonia records the latest event dated at 10,000 years, and the isotopic features, particularly those of helium, are suggesting the presence of magmatic bodies still cooling at depth. The N2-rich gases, i.e. those from the IS area, has an atmospheric origin, as highlighted by the N2/Ar ratio that ranges between those of air and ASW (Air Saturated Water). The isotopic composition of carbon is distinctly negative (down to -21‰ PDB) and that of helium is typically crustal (0.02-0.08 Ra), confirming that these gas discharges are related to a relatively shallow source.

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

This paper proposes a method for the identification of different partial discharges (PDs) 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 classification 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 classification results permits both the identification of different PD sources and the discrimination between original PD signals, reflections, noise and external interferences. 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 license.