Using the Data Mining Based Fuzzy Classification Algorithm for Power Distribution Fault Cause Identification with Imbalanced Data

Power distribution systems reliability is significantly affected by many outage causing events; good outage cause identification can help expedite the restoration procedure. However, the data imbalance issue encountered in many real-world data affects the performance of fault cause identification. The elegant fuzzy classification algorithm, I-algorithm, proposed by Ishibuchi et al. achieves satisfactory performance on many carefully preprocessed data sets but not on the imbalanced data, I-algorithm, an extension of the I-algorithm, is developed in this paper to alleviate the effect of imbalanced data constitution. Both the I- and E-algorithms are applied to Duke Energy outage data for power distribution systems fault cause identification. Their performance on this real-world imbalanced data set is presented, compared, and analyzed to demonstrate the improvement achieved by the extended algorithm