重庆理工大学学报2025，Vol.39Issue(9)：202-210,9.DOI:10.3969/j.issn.1674-8425(z).2025.05.025
多头通道选择与全局特征融合的电能质量扰动分类识别方法
Multi-head channel selection and global feature fusion for power quality disturbance classification and recognition
摘要
Abstract
Power quality disturbances(PQDs)often encounter such problems as low utilization of disturbance information and vulnerability to noise interference.To address these issues,a new model,Residual Densely Connected Atrous Spatial Pyramid Pooling Optimized by Channel Multi-Head Self-Attention Mechanism(RDASPP-CMSA)is built.It is designed specifically for PQD classification.First,the Residual Densely Connected Atrous Spatial Pyramid Pooling(RDASPP)module is employed to extract and integrate multi-scale global features.By incorporating residual connections and BottleNeck structures,the model improves stability and enhances feature perception capabilities.Then,a dynamic weighting feature selection strategy,Channel Multi-Head Self-Attention Mechanism(CMSA),is introduced through the integration of global pooling and multi-head self-attention mechanisms.It facilitates simultaneous feature learning across both channel and temporal dimensions,achieving dynamic weighting and selection of features.Thus,the model effectively suppresses noise and captures critical features within disturbance signals.Finally,a fully connected layer is utilized to perform precise classification of individual disturbance signals.Experimental results reveal the RDASPP-CMSA model achieves a classification accuracy of 99.60%for 29 types of disturbances under a 30 dB noise environment and an accuracy of 99.98%when tested on five real-world grid disturbances.Comparative analysis shows it outperforms existing models in both classification accuracy and robustness against noise,underscoring its efficacy in PQD classification tasks.关键词
电网/电能质量/注意力机制/深度学习Key words
power grid/power quality/attention mechanism/deep learning分类
信息技术与安全科学引用本文复制引用
叶鹏,宋弘,吴浩,江俊卓..多头通道选择与全局特征融合的电能质量扰动分类识别方法[J].重庆理工大学学报,2025,39(9):202-210,9.基金项目
四川省科技厅项目(2022YFS0518,2022ZHCG0035) （2022YFS0518,2022ZHCG0035）
人工智能四川省重点实验室项目(2022RZY01) （2022RZY01）
