Failure rate modeling: A non-parametric data mining approach to MV network field data

Power distribution fault statistics provides valuable knowledge of system failure rate behavior, as the start point of reliability evaluations. Using this statistics, electric utilities trace and develop their reliability plans based on fault statistics. This paper considers a data mining approach to model momentary failure rate in terms of the most influential factors. A methodology is presented here, for momentary fault cause identification, using a feature selection algorithm applied to MV network of the Greater Tehran Electric Distribution Company (GTEDC). Subsequently, two non-parametric failure rate models; classification and regression tree (CART) and artificial neural network (ANN) are utilized to cope with the high non-linearity of the problem space. Results and comparisons of the characteristics of the proposed methods illustrate the advantages and disadvantages of each model.