A generic knowledge-based approach to the analysis of partial discharge data

Partial Discharge (PD) diagnosis is a recognized technique to detect defects within high voltage insulation in power system equipment. A variety of methods exist to capture the signals that are emitted during PD, and this paper focuses on the ultra high frequency (UHF) and IEC 60270 techniques. Phase-resolved patterns can be constructed from the PD data captured using either of these techniques and due to the individual signatures that different defects generate, experts can examine the phase-resolved pattern to classify the defect that created it. In recent years, knowledge regarding PD phenomena and phase-resolved patterns has increased, providing an opportunity to employ a knowledge-based system (KBS) to automate defect classification. Due to the consistent physical nature of PD across different high voltage apparatus and the ability to construct phase-resolved patterns from various sensors, the KBS offers a generic approach to the analysis of PD by taking the phase-resolved pattern as its input and identifying the physical PD processes associate with the pattern. This paper describes the advances of this KBS, highlighting its generic application through the use of several case studies, which present the diagnosis of defects captured through both the IEC 60270 and UHF techniques. This paper also demonstrates, in one of the case studies, how a limitation of previous pattern recognition techniques can be overcome by mimicking the approach of a PD expert when the pulses occur over the zero crossings of the voltage waveform of the phase-resolved pattern.