Current studies on identifying operating data anomalies in power systems are generally inadequate in analyzing inter-data correlations,leading to relatively large deviation of identification results.In view of this the present work stud-ied a fuzzy-rule-based method to identify operating data anomalies in power systems.A multi-layer-structured fuzzy rule was constructed by fully considering inherent attribute characteristics of power system operating data and following the justifiable granularity principle.After segmenting characteristic parameters of the data,the inter-data correlations were determined according to hidden structural information in the dataset.In the stage of data anomalies identification,taking fuzzy rules as judging criteria,the specificity of operating data was calculated and the accuracy of identification was amelio-rated by restrained fuzzy granularity.The proposed identification method was proved by test results capable of achieving i-dentification results whose deviation remained always within 3.0％with respect to operating data with different extents of anomalies.
维普
万方数据