Abstract:- This paper proposes an approach to discover conflicting data in a training set for neural-networkbased transient stability classification of power systems. The conflicting data or inconsistent cases here denote the cases belonging to one class but lying in the regions occupied by another class in the numeric feature space of transient stability classification. Misclassifications usually occur due to the presence of conflicting data. Through defining a group of hyper-cubic regions, the conflicting data is found by direct class-region analysis with continuous numeric features and without feature discretization. The ratio of the conflicting data to all the training data indicates the separability of the feature space in which the training data set is embedded. The numerical results presented show the effectiveness of the proposed approach for discovering the conflicting data of transient stability classification. Key-Words:- power system transient stability, conflicting data analysis, pattern classification, knowledge discovery, neural networks, separability
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