基于自适应时频增强框架的电能质量扰动识别研究OA

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.