Integrated DWT-DHT Feature Set for ABC Optimized SVM-Based PQ Classifier

Abstract

The power quality (PQ) literature contains abundant methodologies for the classification of PQ disturbances that show the significance of the problem and the necessity to design a reliable method that will accurately and quickly identify the type of disturbance. However, most of the available methods are not suitable for real-time applications as they are sensitive to noise, require more memory, computationally complex, and are also time-consuming. This work suggests an approach using Artificial Bee Colony Algorithm (ABC) for concurrent feature and classifier parameters selection to reduce execution time and enhance classification accuracy. An integrated feature set consisting of features extracted using two signal processing techniques namely Discrete Wavelet Transform (DWT) and Discrete Hilbert Transform (DHT) is proposed for clear discrimination of disturbances in contrast to the conventional single feature extraction tool commonly employed. To authenticate the effectiveness of the proposed dual set features, this method has been tested on both simulated and real-time PQ data and is found to perform better than methods using a single signal processing tool.

Keywords:

• feature extraction
• Hilbert transform
• power quality
• support vector machine
• wavelet transform

Subject classification codes:

• Power quality

Additional information

Notes on contributors

K. Manimala

K. Manimala earned a Bachelor of Engineering in Electrical and Electronics Engineering in 1994 from Government College of Engineering, Tirunelveli, India. She earned her Master’s degree in Computer Science and Engineering from Manonmaniam Sundaranar University, Tirunelveli, India in 2004. She was awarded PhD for her research work in Power Quality data mining during 2013 by Anna University, Chennai. She has 26 years of teaching experience. She is currently working as Professor in EEE Department of Dr. Sivanthi Aditanar College of Engineering, Tiruchendur, India. She has obtained funded projects from funding agencies like DRDO. She has published many national, international journal papers, international and national conference papers. Her research interests include electric power components and systems.

K. Selvi

K. Selvi obtained B.E.(EEE) with Honors, M.E.(Power System) with distinction, from Madurai Kamaraj University in the year 1989 and 1995, respectively. She obtained her Ph.D. in Electricity Deregulation in June 2005 from Madurai Kamaraj University. She has nearly 32 years of teaching experience. She is currently working as Professor in the Dept. of Electrical Engineering, in Thiagarajar College of Engineering, Madurai, Tamil Nadu, India. She has published many national, international journal papers, international and national conference papers. She has obtained Young Scientist Fellowship from Dept. of Science and Technology DST, India. She has obtained funded research projects from funding agencies like DST. Her research interests are electricity deregulation and AI techniques.