Classification of Power Quality Disturbances using Mahalanobis Distance Classifier and Stockwell Transformation

Main Article Content

Md. Jashim Uddin Bhuiyan
Mollah Rezaul Alam


Detection and classification of PQ (Power Quality) disturbances in distribution/transmission systems are very important for protection of electricity network. Most of the disturbances of power network are non-stationary and momentary in nature, hence it requires advanced tools and techniques for the analysis and classification of PQ disturbances. This paper presents the detection and classification of PQ events or disturbances employing Stockwell-Transformation, known as S-Transformation, and Mahalanobis Distance (MD) based approach. The proposed method exploits only four features extracted through S-transformation of the voltage signal; then, using these four features, classification is conducted by MD based classifier. In this work, classification of several PQ disturbances, such as, voltage sags, swells, harmonics, notch, flicker, transient oscillation, momentary interruption, etc., are considered. The simulation results demonstrate that the proposed method is very effective and accurate in detecting and classifying PQ events. Validation of the proposed approach has also been conducted using real signal recorded in IEEE 1159.2 database. Moreover, comparative classification performance of MD based classifier
with MED (minimum Euclidean distance) and LVQ (learning vector quantization) reveals the superiority of the proposed approach.

Article Details

How to Cite
M. J. U. Bhuiyan and M. R. Alam, “Classification of Power Quality Disturbances using Mahalanobis Distance Classifier and Stockwell Transformation”, AJSE, vol. 17, no. 1, pp. 19-24, Mar. 2018.
Author Biography

Mollah Rezaul Alam, Assistant Professor, American International University-Bangladesh (AIUB)

Mollah Rezaul Alam received the B.Sc. degree in electrical and electronic engineering from Bangladesh University of Engineering and Technology (BUET), Bangladesh in 2005, and the Ph.D. degree in Electrical and Electronic Engineering from University of Wollongong, New South Wales, Australia in 2015. Currently, he is an
Assistant Professor in the EEE department of American International University- Bangladesh (AIUB). Prior to completing Ph.D. degree, he was involved in the telecommunication industry in Bangladesh for 5 years, where he worked in the area of Intelligent Network & Value Added Services of cellular mobile technology. His research interests include computational intelligence, data mining, fault detection, classification and analysis considering the impacts of distributed energy resources.