AUTHOR=Molu Reagan Jean Jacques , Mbasso Wulfran Fendzi , Saatong Kenfack Tsobze , Dzonde Naoussi Serge Raoul , Alruwaili Mohammed , Elrashidi Ali , Nureldeen Waleed TITLE=Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach JOURNAL=Frontiers in Energy Research VOLUME=12 YEAR=2024 URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2024.1435704 DOI=10.3389/fenrg.2024.1435704 ISSN=2296-598X ABSTRACT=

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.