Power quality data mining using soft computing and wavelet transform

This paper presents a new approach to power quality data mining using a modified wavelet transform for feature extraction of power disturbance signal data and a fuzzy multilayer perceptron network to generate the rules and classify the patterns. The choice of modified wavelet transform known as multiresolution s-transform is essential for processing very short duration nonstationary time series data from transient disturbances occurring on an electric supply network as they can not be handled by conventional Fourier and other transform methods for extraction of relevant features pertinent for data mining applications. The trained fuzzy neural network infers the output class membership value of an input pattern and a certainty measure is also presented to facilitate rule generation. Using the electric supply network disturbance data obtained from numerical algorithms and MATLAB software, the paper presents transient disturbance pattern classification scores. A knowledge discovery approach is also highlighted in the paper to convert raw power disturbance signal data to knowledge in the form of an answer module to the queries by the end-users. The pattern classification approach used in this paper can also be applied to speech, cardiovascular system and other medical and engineering databases.