HHT-based classification of composite power quality events
International Journal of Energy Sector Management
ISSN: 1750-6220
Article publication date: 27 May 2014
Abstract
Purpose
An electrical power system is expected to deliver undistorted sinusoidal, rated voltage and current continuously to the end-users. The problem of power quality (PQ) occurs when there is (are) deviation(s) in voltage and/or current which cause(s) failure or mal-operation of the customer's equipments. Various methods are suggested to detect and classify single PQ event in a power system, the performance of such methods to classify composite PQ events is limited. The purpose of this paper is the classification of composite PQ events in emerging power systems.
Design/methodology/approach
This paper proposes an effective method to classify composite PQ events using Hilbert Huang transform (HHT). The performance of probabilistic neural network (PNN) classifier and support vector machine (SVM) classifier to efficiently classify composite PQ events is compared.
Findings
The features extracted from HHT are simple yet effective. SVMs and PNN classifiers are used for PQ classification. It is found that PNN classifier outperforms SVM with the classification accuracy of 100 percent.
Originality/value
Different PQ signals used for analysis are generated by simulating a practical distribution system of an Indian academic institution.
Keywords
Citation
Saxena, D., Singh, S.N., Verma, K.S. and K. Singh, S. (2014), "HHT-based classification of composite power quality events", International Journal of Energy Sector Management, Vol. 8 No. 2, pp. 146-159. https://doi.org/10.1108/IJESM-02-2013-0001
Publisher
:Emerald Group Publishing Limited
Copyright © 2014, Emerald Group Publishing Limited