Abstract
With the development of smart grid, data mining makes identification of users of electricity stealing and leakage a reality. This paper systematically studied the data of electricity information for unusualness detection. Firstly, it analyzed the behavior features of those users suspicious of electricity stealing and leakage, constructed a preliminary behavior identification index system. Then it used the discrimination analysis and correlation analysis method for quantitative judgment on the feasibility of screening evaluation index, excluding some indexes, finally got three first level indexes and eight secondary indexes. The paper presented three new indexes, including coefficient of dispersion of A&C phase current’s (voltage’s) difference and the clustering number of monthly electricity load pattern. Calculation method of the index was also described in detail. The index system has shown its feasibility by application of Clustering method and the fuzzy neural network.
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