Discovery based Classification Method for Assessment
Dynamic security assessment (DSA) is an important issue in modern power system security analysis. This paper proposes a novel pattern discovery (PD) based fuzzy classification scheme for the DSA. Firstly, the PD algorithm is improved by integrating the proposed centroid deviation analysis technique and the prior knowledge of the training data set. This improvement can enhance the performance when apply it to extract the patterns of data from a training data set.
Secondly, based on the results of the improved PD algorithm, a fuzzy logic-based classification method is developed to predict the security index of a given power system operating point. In addition, the proposed scheme is tested on the IEEE 50-machine system and is compared with other state-of-the-art classification techniques. The comparison demonstrates that the proposed model is more effective in the dynamic security assessment of a power system.
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