Recognition and classification of power quality disturbances by DWT-MRA and SVM classifier


Electrical power system is a large and complex network, where power quality disturbances (PQDs) must be monitored, analyzed and mitigated continuously in order to preserve and to re-establish the normal power supply without even slight interruption. Practically huge disturbance data is difficult to manage and requires the higher level of accuracy and time for the analysis and monitoring. Thus automatic and intelligent algorithm based methodologies are in practice for the detection, recognition and classification of power quality events. This approach may help to take preventive measures against abnormal operations and moreover, sudden fluctuations in supply can be handled accordingly. Disturbance types, causes, proper and appropriate extraction of features in single and multiple disturbances, classification model type and classifier performance, are still the main concerns and challenges. In this paper, an attempt has been made to present a different approach for recognition of PQDs with the synthetic model based generated disturbances, which are frequent in power system operations, and the proposed unique feature vector. Disturbances are generated in Matlab workspace environment whereas distinctive features of events are extracted through discrete wavelet transform (DWT) technique. Machine learning based Support vector machine classifier tool is implemented for the classification and recognition of disturbances. In relation to the results, the proposed methodology recognizes the PQDs with high accuracy, sensitivity and specificity. This study illustrates that the proposed approach is valid, efficient and applicable.

Publication DOI:
Divisions: College of Engineering & Physical Sciences
Additional Information: Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Uncontrolled Keywords: Discrete wavelet transform,Multi resolution analysis,Power quality disturbances,Support vector machine,Computer Science(all)
Publication ISSN: 2156-5570
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://thesai. ... csa&SerialNo=48 (Publisher URL)
PURE Output Type: Article
Published Date: 2019-01-01
Accepted Date: 2019-01-01
Authors: Jandan, Fayyaz
Khokhar, Suhail
Shaha, Syed Abid Ali
Abbasi, Farhan



Version: Published Version

License: Creative Commons Attribution

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