Data mining on partial discharge signals of power transformer's defect models

• Published in: International transactions on electrical energy systems Vol. 23; no. 3; pp. 423 - 437
• Main Authors: Darabad, V. P., Vakilian, M., Blackburn, T. R., Phung, B. T.
• Format: Journal Article
• Language: English
• Published: Blackwell Publishing Ltd 01.04.2013
• Subjects: data mining; partial discharge; power transformer defect
• ISSN: 2050-7038; 2050-7038
• DOI: 10.1002/etep.1614

SUMMARY Partial discharge (PD) is a common phenomenon that occurs in the insulation of high‐voltage equipment, such as transformers, and has a damaging effect on the insulation. If data mining techniques are used to extract special specifications and features for different types of PDs in power transformers or other power equipment, one can diagnose the insulation condition of such equipment by its continuous online monitoring. These diagnosis results can be used to develop precise preventive measures and maintenance procedures. This maintenance would cost less and can be carried out in a shorter time. Consequently, the lifetime expectancy of the transformers will be improved. In this article, experimental models are developed to simulate some of the PD types that can occur in a power transformer. Using these models, some of the features that can differentiate those PD types are extracted through texture analysis. Two classification algorithms are used for the separation of PD sources, decision tree and k‐nearest neighbors algorithm. It is anticipated that these models can help us to identify the type of PD fault in a transformer when the required PD measured data are available. Copyright © 2012 John Wiley & Sons, Ltd.