Advanced Pattern Discovery-based Fuzzy Classification Method for Power System Dynamic Security Assessment


Dynamic security assessment (DSA) is an important issue in trendy power system security analysis. This paper proposes a unique pattern discovery (PD)-based fuzzy classification theme for the DSA. First, the PD algorithm is improved by integrating the proposed centroid deviation analysis technique and also the previous knowledge of the training knowledge set. This improvement can enhance the performance when it's applied to extract the patterns of data from a training information set. Secondly, primarily based on the results of the improved PD algorithm, a fuzzy logic-based classification method is developed to predict the protection index of a given power system operating point. Additionally, the proposed scheme is tested on the IEEE fifty-machine system and is compared with other state-of-the-art classification techniques. The comparison demonstrates that the proposed model is additional effective within the DSA of an influence system.

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