Partial discharge pattern classification using the fuzzy decision tree approach

Partial discharge (PD) measurement is a proven flaw detection technique for finding cavities that are defects in the insulating material. In this paper, a novel approach for the classification of cavity sizes, based on their maximum PD charge transfer-applied voltage (Δ Q-V) characteristies using a fuzzy decision tree system, is proposed. The (Δ Q-V) partial discharge patterns for different cavity sizes are represented by features extracted from their pulse shapes, and the classification tules are directly extracted from the data using the decision tree. The decision tules obtained from the decision tree are then converted to the fuzzy IF-then rules, and the back-propagation algorithm is utilized to tune the paramaters of the membership functions employed in the fuzzy classifier. The neuro-fuzzy classification technique is shown to provide successful classification of void sizes in an easily interpretive fashion.