Descriptive Data Mining of Partial Discharge Using Decision Tree With Genetic Algorithm

Partial discharge (PD) is a common phenomenon that occurs in insulation of high voltage equipment such as transformers and has a damaging effect to the insulation. In this paper, the application of descriptive data mining on PD occurring in insulating systems is shown. Experiments were set up to create three basic types of PD: corona, surface discharges and internal discharges. PD data were analysed using phase resolved analysis and pulse height analysis. Descriptive data mining was applied on the collected data using decision tree with genetic algorithm (GA) to mine the rules/relationships that can be used to differentiate the PD. These extracted rules are useful as input to predictive data mining such as fuzzy logics.