摘要
电能质量扰动信号类型多样且特征耦合性强,这给电能质量扰动识别和分类带来了极大挑战.为了提高复杂环境下电能质量扰动信号的识别精度,提出一种基于多头卷积注意力网络的电能质量扰动识别算法.首先,为了减少模型的复杂度,设计多尺度残差卷积模块重建扰动信号特征;然后,为了解决因卷积核感受野有限导致的识别不清等问题,设计了多头卷积注意力模块捕捉扰动信号时间序列的全局依赖关系,实现扰动信号全局特征与不同尺度的局部特征相融合;最后,利用全连接层将高维的特征信息映射到低维空间,并通过Softmax分类器实现电能质量扰动信号识别与分类.结果表明,该文所提算法能够快速提取单一和复合电能质量扰动信号的特征;与其他算法相比,所提算法在不同信噪比下均具有较高的识别准确率,验证了其鲁棒性.
The diverse types of power quality disturbance signals and strong feature coupling bring enormous challenges to power quality disturbance identification and classification.To improve the recognition accuracy of power quality disturbance signals in complex environments,a power quality disturbance recognition algorithm based on a multi-head convolutional attention network was proposed.First,to reduce the complexity of the model,a multi-scale residual convolution module was designed to reconstruct the disturbance signal features.Then,to address issues such as unclear recognition caused by the limited sensory field of the convolution kernel,a multi-head convolutional attention module was designed to capture the global dependency of the disturbance signal time series and achieve the fusion of the global features of the disturbance signal with local features of different scales.Finally,a fully connected layer was used to map the high-dimensional feature information to low-dimensional space,and the Softmax classifier was applied to identify and classify the power quality disturbance signals.The experimental results showed that the proposed algorithm extracted the features of single and composite power quality disturbance signals effectively and demonstrated higher accuracy under different SNRs compared with other algorithms,verifying the robustness of the algorithm.
作者
安林
郑玉平
竺德
李明胜
高清维
AN Lin;ZHENG Yuping;ZHU De;LI Mingsheng;GAO Qingwei(State Key Laboratory of Technology and Equipment for Defense Against Power System Operational Risks,Nanjing 211106,China;State Grid Nari Technology Co.,Ltd.,Nanjing 211106,China;School of Electrical Engineering and Automation,Anhui University,Hefei 230601,China;Power Quality Engineering Research Center,Ministry of Education,Anhui University,Hefei 230601,China)
出处
《安徽大学学报(自然科学版)》
北大核心
2025年第4期57-65,共9页
Journal of Anhui University(Natural Science Edition)
基金
电网运行风险防御技术与装备全国重点实验室科研项目(SGNR0000KJJS2302144)。
