Table6_Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach.docx

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.

电能质量事件监测对于维持电网稳定可靠运行至关重要。本文提出了一种集成离散小波变换（Discrete Wavelet Transform, DWT）与极限学习机（Extreme Learning Machine, ELM）的新型系统，用于电能质量扰动的检测与分类。离散小波变换可通过多分辨率分析将信号分解为时频分量，能够捕获电压暂降、电压暂升和谐波等各类电能质量扰动。基于这些分解后信号训练得到的极限学习机分类器，分类准确率可达99.69%，显著优于结合支持向量机的短时傅里叶变换（Short-Time Fourier Transform, STFT，97.22%）以及结合人工神经网络的快速傅里叶变换（Fast Fourier Transform, FFT，99.30%）等传统方法。本系统在赛灵思Zynq-7000 SoC FPGA上完成了验证，展现出实时处理能力，其延迟为1.5毫秒，功耗为1.8瓦。上述研究结果凸显了所提方法在实时、高精度且高能效的电能质量监测领域的有效性。