Partial discharge pulse pattern recognition using an inductive inference algorithm

This paper presents a novel approach in the area of time dependent partial discharge (PD) pulse pattern recognition, to applications based on the inductive learning (decision tree) approach. Different attributes based on pulse shape analysis are used as representative feature vectors that can accurately capture the unique and salient characteristics of the PD pulse shape. In the training phase, a decision tree is developed to relate the pulse shape with the cavity size by using inductive machine learning. The C4.5 machine learning algorithm is deployed to realize the tree using the training data, since it has the capability of inferring the rules and to produce the tree in terms of continuous features. During testing, the cavity size is recognized by means of the rules extracted from the decision tree. The dependency between the features and the classes are examined using the mutual information approach. The proposed algorithm possesses the inherent advantage of explaining the result via the self-created rule base as demonstrated by the results obtained. Those self-created rules can be employed as the basis for applying a fuzzy expert system for the classification of void sizes in an easily interpreted fashion.