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

Surendra Singh, Avdhesh Sharma, Akhil Ranjan Garg, Om Prakash Mahela, Baseem Khan (), Ilyes Boulkaibet (), Bilel Neji, Ahmed Ali and Julien Brito Ballester
Additional contact information
Surendra Singh: Department of Electrical Engineering, MBM University, Jodhpur 342001, India
Avdhesh Sharma: Department of Electrical Engineering, MBM University, Jodhpur 342001, India
Akhil Ranjan Garg: Department of Electrical Engineering, MBM University, Jodhpur 342001, India
Om Prakash Mahela: Power System Planning Division, Rajasthan Rajya Vidyut Prasaran Nigam Ltd., Jaipur 302005, India
Baseem Khan: Engineering Research and Innovation Group (ERIG), Universidad Internacional Iberoamericana, Campeche 24560, Mexico
Ilyes Boulkaibet: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Bilel Neji: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Ahmed Ali: Department of Electrical and Electronic Engineering Technology, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg P.O. Box 524, South Africa
Julien Brito Ballester: Faculty of Social Sciences and Humanities, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain

Sustainability, 2023, vol. 15, issue 5, 1-30

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.

Keywords: discrete wavelet transform; distribution system; Hilbert transform; power quality event; rule-based decision tree; Stockwell transform (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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