Abstract

This paper presents a new approach for power signal time series data mining using S-transform based K-means clustering technique and fuzzy decision tree. 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 fuzzy decision tree for pattern classification of time varying database like the power signal data bases.

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