Descriptive data mining of partial discharge using decision tree with genetic algorithm

Partial discharge (PD) is a common phenomenon which occurs in insulation of high voltage equipments such as transformers and has a damaging effect to the insulation. In this paper, the application of descriptive data mining on PD occuring in insulating system is shown. Experiments were set up to create three basic types of PD: corona, surface discharges and internal discharges. Partial discharge 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 which can be used to differentiate the PD. These extracted rules are useful as input to predictive data mining such as fuzzy logics.