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Through investigations made on crosslinked polyethylene (XLPE) insulated power cables that had experienced failures, blue-colored water trees were observed in addition to ordinary white water trees. The blue water tree was found to be much more harmful than the white water tree, since more electrical trees started at the blue trees. Elemental analysis revealed that iron and sulfur exist in the blue tree. In the case of this blue water tree, space charge exists abundantly but only at the tree tip, while no charge signal is observed on the electrode from which the tree starts. This indicates that the conductivity of the blue tree is very high. This is considered to be the main reason of the harmfulness of this tree.

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This paper describes an experimental study of the discrimination of partial discharge (PD) signals from external noise in a crosslinked polyethylene (XLPE) cable by using a neural network (NN) system. Measurement of PD signal and external noise was carried out with a PD pulse recorder for a 66kV XLPE cable with an artificial defect and a drill. The NN was a three-layer artificial neural system with feedforward connections, and its learning method was a back-propagation algorithm.Its input infomation was a combination of the discharge magnitude, the number of pulse counts, and the phase angle of applied voltage.The NN was able to discriminate unknown input patterns with 89% correct responses after learning PD patterns that included external noise.In this case, the NN could correctly discriminate all unknown input patterns for a signal-to-noise ratio greater than or equal to unity. The duration, including the measurement time, required for the NN to discriminate an input pattern was about 45 s.

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