现代信息科技2026，Vol.10Issue(1)：1-6,12,7.DOI:10.19850/j.cnki.2096-4706.2026.01.001
基于自适应时频增强框架的电能质量扰动识别研究
Research on Power Quality Disturbance Recognition Based on Adaptive Time Frequency Enhancement Framework
摘要
Abstract
To address the issues of fixed feature fusion and high computational complexity in traditional Power Quality Disturbances(PQDs)signal recognition models,an Adaptive Gramian Time Frequency Enhancement Network(AGTFENet)is proposed.Firstly,a noise reduction strategy based on the Gram matrix is introduced to process one-dimensional input signals.A three-branch parallel architecture is adopted to handle the original signal,Gram noise-reduced signal,and frequency spectrum respectively.Secondly,multiple feature learning modules are stacked,and Depthwise Separable Convolution are used to extract features from each branch.Finally,adaptive average pooling and an adaptive weight mechanism are introduced to dynamically adjust the contributions of features from each branch,achieving weighted fusion of features and classification for disturbance signals.Simulation results show that AGTFENet achieved recognition accuracies of 98.9%,98.7%,98.5%,and 97.8%under different noise levels(no noise,40 dB,30 dB,20 dB),respectively,outperforming other classification models.Moreover,benefiting from its lightweight design,it demonstrates excellent performance in terms of computational complexity.关键词
电能质量扰动/格拉姆降噪/自适应机制/深度可分离卷积/扰动识别Key words
PQDs/Gramian Noise Reduction/adaptive mechanism/Depthwise Separable Convolution/disturbance recognition分类
信息技术与安全科学引用本文复制引用
张欣语..基于自适应时频增强框架的电能质量扰动识别研究[J].现代信息科技,2026,10(1):1-6,12,7.基金项目
安徽省自然科学基金项目(2208085QE167) （2208085QE167）
