PD inference for the early detection of electrical treeing in insulation systems

Partial discharge (PD) measurements constitute one of the most promising tools for electrical insulation diagnosis. This paper describes how a procedure based on PD measurements can provide early detection of electrical trees in polymeric insulation systems. Such an application relies upon a new methodology, which provides enhanced tools for the identification of PD generating defects. Tree inference is carried out stepwise. Acquired signals are primarily separated according to their waveform, thus achieving data sets related to a specific PD typology. Then, fuzzy algorithms are applied to PD height and phase derived quantities belonging to these homogeneous data sets, in order to assign a membership degree to specific output categories. If the data set is relevant to an internal defect, a further analysis is performed in order to establish whether or not this defect is a treed region. The algorithm described in this paper was developed resorting to tests performed on artificial test specimens and electrical apparatus. In particular, the rules to detect the presence of electrical trees were derived from experiments carried out on needle-plane objects, constituted by slabs of cross-linked polyethylene (XLPE) where a needle is inserted and partially extracted in order to generate a cavity in front of the needle tip. Tests were also performed on cables having artificial defects, as well as on other insulation systems, such as high frequency transformers. Applications of the proposed approach to MV cables and to RV transformers show that electrical trees can be detected successfully before final breakdown.