Data clustering and storing for wide-area power quality monitoring

This paper presents a new technique that can be implemented for monitoring the quality of serves in wide areas and representing the data in terms of a small set of coefficients. Wavelet multi-resolution analysis (MRA) is utilized for feature extraction and data clustering of different disturbances. The proposed feature vector and the rapid drop off in the number of the wavelet-expansion coefficients are used to classify different power quality problems and to represent the original data in terms of a small set of coefficients.