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This paper presents a new approach for power signal time series data mining using S-transform based K-means clustering technique. Initially the power signal time series disturbance data are pre-processed through an advanced signal processing tool such as S-transform and various statistical features are extracted, which are used as inputs to the K-means algorithm for disturbance event detection. Particle Swarm Optimization (PSO) technique is used to optimize cluster centers which can be inputs to a decision tree for pattern classification. The proposed hybrid PSO-K-Means clustering technique provides accurate classification rates even under noisy conditions compared to the existing techniques, which show the efficacy and robustness of the proposed algorithm for time varying database like the power signal data.
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