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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Process and Energy Systems Engineering
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1435704
This article is part of the Research Topic Novel Control Approaches for Energy Conservation and Conversation Systems View all 4 articles

Enhancing Power Quality Monitoring with Discrete Wavelet Transform and Extreme Learning Machine: A Dual-Stage Pattern Recognition Approach

Provisionally accepted
Reagan Jean Jacques MOLU Reagan Jean Jacques MOLU 1*Wulfran FENDZI MBASSO Wulfran FENDZI MBASSO 1*Kenfack T. Saatong Kenfack T. Saatong 1*Serge Raoul DZONDE NAOUSSI Serge Raoul DZONDE NAOUSSI 1*Mohammed Alruwaili Mohammed Alruwaili 2Ali Elrashidi Ali Elrashidi 3*Waleed Nureldeen Waleed Nureldeen 3*
  • 1 University of Douala, Douala, Littoral, Cameroon
  • 2 Northern Border University, Arar, Northern Borders, Saudi Arabia
  • 3 College of Business Administration, University of Business and Technology, Jeddah, Makkah, Saudi Arabia

The final, formatted version of the article will be published soon.

    Monitoring energy quality events is crucial for maintaining the stability and reliability of power grids. This paper presents a novel system integrating Discrete Wavelet Transform (DWT) and Extreme Learning Machine (ELM) for detecting and classifying power quality disturbances.The DWT performs multi-resolution analysis to decompose signals into time-frequency components, capturing various disturbances such as sags, swells, and harmonics. The ELM classifier, trained on these decomposed signals, achieves an impressive classification accuracy of 99.69%, significantly outperforming conventional methods like STFT with SVM (97.22%) and FFT with ANN (99.30%). The system was validated on a Xilinx Zynq-7000 SoC FPGA, demonstrating real-time processing capabilities with a latency of 1.5 milliseconds and a power consumption of 1.8 watts. These findings highlight the effectiveness of the proposed method for real-time, accurate, and energy-efficient power quality monitoring.

    Keywords: Energy Quality Monitoring, Power quality disturbances, Discrete wavelet transform, Extreme learning machine, FPGA IMPLEMENTATION, real-time processing

    Received: 20 May 2024; Accepted: 21 Jun 2024.

    Copyright: © 2024 MOLU, FENDZI MBASSO, Saatong, DZONDE NAOUSSI, Alruwaili, Elrashidi and Nureldeen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Reagan Jean Jacques MOLU, University of Douala, Douala, Littoral, Cameroon
    Wulfran FENDZI MBASSO, University of Douala, Douala, Littoral, Cameroon
    Kenfack T. Saatong, University of Douala, Douala, Littoral, Cameroon
    Serge Raoul DZONDE NAOUSSI, University of Douala, Douala, Littoral, Cameroon
    Ali Elrashidi, College of Business Administration, University of Business and Technology, Jeddah, 21361, Makkah, Saudi Arabia
    Waleed Nureldeen, College of Business Administration, University of Business and Technology, Jeddah, 21361, Makkah, Saudi Arabia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.