Power Quality Detection and Categorization Algorithm Actuated by Multiple Signal Processing Techniques and Rule-Based Decision Tree

Author:

Singh Surendra1,Sharma Avdhesh1,Garg Akhil Ranjan1ORCID,Mahela Om Prakash23ORCID,Khan Baseem34ORCID,Boulkaibet Ilyes5,Neji Bilel5ORCID,Ali Ahmed6,Brito Ballester Julien78

Affiliation:

1. Department of Electrical Engineering, MBM University, Jodhpur 342001, India

2. Power System Planning Division, Rajasthan Rajya Vidyut Prasaran Nigam Ltd., Jaipur 302005, India

3. Engineering Research and Innovation Group (ERIG), Universidad Internacional Iberoamericana, Campeche 24560, Mexico

4. Department of Electrical Engineering, Hawassa University, Hawassa 1530, Ethiopia

5. College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait

6. Department of Electrical and Electronic Engineering Technology, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg P.O. Box 524, South Africa

7. Faculty of Social Sciences and Humanities, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain

8. Faculty of Social Sciences and Humanities, Universidad Internacional Iberoamericana, Campeche 24560, Mexico

Abstract

This paper introduces a power quality (PQ) detection and categorization algorithm actuated by multiple signal processing techniques and rule-based decision tree (RBDT). This is aimed to recognize PQ events of simple nature and higher order multiplicity with less computational time using hybridization of the signal processing techniques. A voltage waveform with a PQ event (PQE) is processed using the Stockwell transform (ST) to compute the Stockwell PQ detection index (SPDI). The voltage waveform is also processed using the Hilbert transform (HT) to compute the Hilbert PQ detection index (HPDI). A voltage waveform is also decomposed using the Discrete Wavelet transform (DWT) to compute the classification feature index (CFI) [CFI1 to CFI4]. A combined PQ detection index (CPDI) is computed by multiplication of the SPDI, the HPDI and CFI1 to CFI4. Incidence of a PQE on a voltage signal is located with the help of a location PQ disturbance index (LPDI) which is computed by differentiating the CPDI with respect to time. CFI5, CFI6 and CFI7 are computed from the SPDI, the HPDI and the CPDI, respectively. Categorization of PQ events is performed using CFI1 to CFI7 by the rule-based decision tree (RBDT) with the help of simple decision rules. We conclude that the proposed algorithm is effective to identify the PQE with an accuracy of 98.58% in a noise-free environment and 97.62% in the presence of 20 dB SNR (signal-to-noise ratio) noise. Ten simple nature PQEs and eight combined PQ events (CPQEs) with multiplicity of two, three and four are effectively detected and categorized using the algorithm. The algorithm is also tested to detect a sag PQ event due to a line-to-ground (LG) fault incident on a practical distribution utility network. The performance of the investigated method is compared with a DWT-based technique in terms of accuracy of classification with and without noise, maximum computational time of PQ detection and multiplicity of PQE which can be effectively detected. A simulation is performed using the MATLAB software. MATLAB codes are used for modelling the PQE disturbances and the proposed algorithm using mathematical formulations.

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

Reference24 articles.

1. Detection and classification of power quality disturbances using GWO ELM;Subudhi;J. Ind. Inf. Integr.,2021

2. A critical and comprehensive review on power quality disturbance detection and classification;Khetarpal;Sustain. Comput. Inform. Syst.,2020

3. A critical review of detection and classification of power quality events;Mahela;Renew. Sustain. Energy Rev.,2015

4. Review on detection and classification of underlying causes of power quality disturbances using signal processing and soft computing technique;Bonde;Mater. Today Proc.,2022

5. Hybrid demodulation concept and harmonic analysis for single/multiple power quality events detection and classification;Kapoor;Int. J. Electr. Power Energy Syst.,2011

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