A self-organizing learning array system for power quality classification based on wavelet transform

• Published in: IEEE transactions on power delivery Vol. 21; no. 1; pp. 286 - 295
• Main Authors: He, H., Starzyk, J.A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2006; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Classification; Computer simulation; Data mining; Disturbances; Disturbances. Regulation. Protection; Electric, optical and optoelectronic circuits; Electrical engineering. Electrical power engineering; Electrical power engineering; Electronics; Entropy; Exact sciences and technology; Feature extraction; Learning; Multiresolution analysis; Neural networks; Noise; Operation. Load control. Reliability; Organizing; Power networks and lines; Power quality; power quality (PQ); self-organizing learning array (SOLAR); Solar power generation; Solar system; Studies; support vector machine (SVM); Support vector machine classification; Support vector machines; Wavelet; Wavelet analysis; wavelet transform; Wavelet transforms; Performance evaluation; Monte Carlo method; Automatic classification; power quality (PQ); Noise; support vector machine (SVM); wavelet transform; Support vector machine; Multiresolution analysis; Experimental result; Self organization; Gaussian noise; Electrical network; Wavelet transformation; White noise; Numerical simulation; Learning algorithm; Service quality; Comparative study; Pattern extraction; self-organizing learning array (SOLAR)
• ISSN: 0885-8977; 1937-4208
• DOI: 10.1109/TPWRD.2005.852392

This paper proposed a novel approach for the Power Quality (PQ) disturbances classification based on the wavelet transform and self organizing learning array (SOLAR) system. Wavelet transform is utilized to extract feature vectors for various PQ disturbances based on the multiresolution analysis (MRA). These feature vectors then are applied to a SOLAR system for training and testing. SOLAR has three advantageous over a typical neural network: data driven learning, local interconnections and entropy based self-organization. Several typical PQ disturbances are taken into consideration in this paper. Comparison research between the proposed method, the support vector machine (SVM) method and existing literature reports show that the proposed method can provide accurate classification results. By the hypothesis test of the averages, it is shown that there is no statistically significant difference in performance of the proposed method for PQ classification when different wavelets are chosen. This means one can choose the wavelet with short wavelet filter length to achieve good classification results as well as small computational cost. Gaussian white noise is considered and the Monte Carlo method is used to simulate the performance of the proposed method in different noise conditions.