基于渐进性特征融合的变电站异常状态检测网络  

Substation Anomaly State Detection Network Based on Asymptotic Feature Fusion

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作  者:李亚霖 李斌[1] 朱新山[1] 钱统玉 王帅[1] 李冠争 LI Yalin;LI Bin;ZHU Xinshan;QIAN Tongyu;WANG Shuai;LI Guanzheng(Key Laboratory of Smart Grid of Ministry of Education(Tianjin University),Nankai District,Tianjin 300072,China)

机构地区:[1]智能电网教育部重点实验室(天津大学),天津市南开区300072

出  处:《电网技术》2025年第4期1658-1667,共10页Power System Technology

基  金:国家电网有限公司科技项目(面向智能电网运维场景的视听觉主动感知与协同认知技术研究及应用)(5600-202046347A-0-0-00)。

摘  要:随着电网规模的不断扩大,提升电网建设过程中的现场作业风险管控能力愈加重要。为了解决安全监督中作业流程监控及违章辨识防控体系智能化程度不高的难题,该文基于YOLOv7构建了一个端到端的变电站异常状态检测网络(asymptotic feature fusion network,AFFNet)。针对变电站异常状态多样,主干网络特征提取能力不足的问题,设计了S-ELAN网络,通过控制最短和最长的梯度路径,使网络能够学习到更多的变电站异常状态特征。针对不同异常状态特征尺度变化大导致的信息丢失问题,设计了渐进性特征融合策略,避免非相邻特征之间较大的语义差距。同时考虑到在每个空间位置进行特征融合时可能出现多目标信息冲突的情况,采用自适应空间融合来降低特征信息的不一致性。进一步,对于数据集中正负样本数量不均衡,引入梯度均衡损失函数,利用梯度信息自适应调整不同样本的权重信息。在变电站异常状态数据集上测试所提方法的效果,整体测试精度达到82.06%,显著优于现有的一阶段检测网络。结果表明所提方法能够准确辨识变电站异常状态,提高了变电站的风险排查和防护能力。消融实验的结果验证了渐进性特征融合策略的有效性。With the continuous development of the power grid,it is becoming increasingly important to enhance the on-site risk management and control capabilities in substation.In order to address the problem of low intelligence in the safety supervision,this paper proposes an end-to-end substation anomaly state detection network called AFFNet(Asymptotic Feature Fusion Network)based on YOLOv7.To address the problem of diverse types and insufficient feature extraction capabilities,the S-ELAN network is designed to learn more features by controlling the shortest and longest gradient paths.In order to avoid the information loss caused from the significant semantic gap between non-adjacent features,a progressive feature fusion strategy is designed to avoid the issue.Furthermore,an adaptive spatial fusion method is utilized to reduce the inconsistency of feature information at each spatial position.In addition,considering the problem of imbalanced positive and negative samples in the dataset,the GHM(Gradient Harmonizing Mechanism)loss function is introduced to adaptively adjust the weight information of different samples with the gradient information.The AFFNet is tested on the substation anomaly state dataset,and the overall accuracy of the proposed model reaches 82.06%,which is significantly better than existing one-stage detection networks.The result demonstrates that the AFFNet model can accurately identify substation anomaly states and improve the risk investigation and protection capabilities of substations.And the ablation results verify that the asymptotic feature fusion is effective for the recognition of the substation anomaly states.

关 键 词:变电站 异常状态 渐进性特征 目标检测 深度学习 

分 类 号:TM721[电气工程—电力系统及自动化]

 

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