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

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3 Author(s)
Le Xu ; Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC ; Mo-yuen Chow ; Leroy S. Taylor

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

Published in:

2006 IEEE PES Power Systems Conference and Exposition

Date of Conference:

Oct. 29 2006-Nov. 1 2006