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The paper records the results of an extensive investigation conducted in Italy regarding the setting up of testing procedures for the assessment of the failure mode behaviour of polymeric housed distribution type surge attesters. Different sample preparation methods and short circuit test procedures are compared and discussed. The evaluation of the performance takes into consideration all the major hazard issues, such as the ejection of fragments, the detachment of the connections and fire ignition. Several aspects to be taken into account for the standardization of the failure mode test are also addressed.

Laboratory tests were carried out to investigate the thermal performance of surge arresters, under contamination and wetting environmental conditions, which lead to non uniform voltage stresses along the arresters. The validity and repeatability of various laboratory test procedures have been examined. The results were also analyzed by means of a mathematical and of a physical model.

The state of health of an electrical cable may be difficult to know, without destructive or very expensive tests. To overcome this, partial discharge (PD) measurements have been proposed as a relatively economic and simple-to-apply experimental technique for retrieving information on the state of health of an electrical cable. The retrieval is based on a relationship between PD measurements and the health of the cable. Given the difficulties in capturing such relationship by analytical models, empirical modeling techniques based on experimental data have been propounded. In this view, a set of PD measurements have been collected by Enea Ricerca sul Sistema Elettrico-ERSE during past campaigns, for building a diagnostic system of electrical cable health state. These experimental data may contain contradictory information which remarkably reduces the performance of the state classifier, if not a priori identified and possibly corrected. In the present paper, 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. [ABSTRACT FROM AUTHOR]