Knowledge-based diagnosis of partial discharges in power transformers

The abstraction of meaningful diagnostic information from raw condition monitoring data in domains where diagnostic expertise and knowledge is limited and constantly evolving presents a significant research challenge. Expert diagnosis and location of partial discharges in high voltage electrical plant is one such domain. This paper describes the functionality of a knowledge-based decision support system capable of providing engineers with a comprehensive diagnosis of the defects responsible for partial discharge activity detected in oil-filled power transformers. Plant data captured from partial discharge (PD) sensors can be processed to generate phase-resolved partial discharge (PRPD) patterns. This paper proposes a means of abstracting the salient features characterizing the observed PRPD patterns. Captured knowledge describing the visual interpretation of these patterns can be applied for defect diagnosis and location. The knowledge-based PRPD pattern interpretation system can support on-line plant condition assessment and defect diagnosis by presenting a comprehensive diagnosis of PD activity detected and classification of the defect source. The paper also discusses how the system justifies its diagnosis of the PD activity to offer the expert greater confidence in the result, a feature generally absent in 'black-box' pattern recognition techniques. The incremental approach exhibited by the system reflects that of a PD expert's visual interpretation of the PRPD pattern. The paper describes how this functional system design has evolved from the approach taken by PD experts to the visual interpretation of PRPD patterns.