Electrical cables diagnostics using an experimental dataset of Partial Discharge measurements containing contradictory patterns

Partial Discharge (PD) measurements have been proposed as a relatively economic and simple-to-apply experimental technique for retrieving information on the health state of an electrical cable. A set of PD measurements have been collected by Enea Ricerca sul Sistema Elettrico (ERSE), for building a diagnos-tic system of electrical cable health state. These experimental data may contain contradictory information which remarkably reduce the performance of the state classifier. In the present work, a novel technique based on the Adaboost algorithm is proposed for identifying contradictory PD patterns within an a priori analysis aimed at improving the diagnostic performance. Adaboost is a bootstrap-inspired, ensemble-based algorithm which has been effectively used for addressing classification problems.