Frontiers in Energy Research (Sep 2024)

Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach

  • Reagan Jean Jacques Molu,
  • Wulfran Fendzi Mbasso,
  • Kenfack Tsobze Saatong,
  • Serge Raoul Dzonde Naoussi,
  • Mohammed Alruwaili,
  • Ali Elrashidi,
  • Waleed Nureldeen

DOI
https://doi.org/10.3389/fenrg.2024.1435704
Journal volume & issue
Vol. 12

Abstract

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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 W. These findings highlight the effectiveness of the proposed method for real-time, accurate, and energy-efficient power quality monitoring.

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