| 面向电力系统的多粒度隐患检测方法 |
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| 引用本文： | 徐晓华,钱平,王一达,周昕悦,徐汉麟,徐李冰. 面向电力系统的多粒度隐患检测方法[J]. 北京航空航天大学学报, 2021, 47(3): 520-530. DOI: 10.13700/j.bh.1001-5965.2020.0491 |
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| 作者姓名： | 徐晓华 钱平 王一达 周昕悦 徐汉麟 徐李冰 |
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| 作者单位： | 1.国网浙江省电力有限公司杭州供电公司, 杭州 310020 |
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| 基金项目： | 国网浙江省电力有限公司科技项目 |
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| 摘 要： | 由于电力系统的安全问题往往会造成严重的经济或社会影响，隐患检测已成为电力系统不可或缺的重要环节。随着人工智能领域的发展，基于深度学习的智能化电力系统隐患检测技术逐渐得到越来越多的关注。但目前的方法大多只是单一地考虑图像的全局特征或局部特征，无法全面彻底表征图像，进而难以捕捉电力领域尤其室外复杂背景下的隐患检测。为此，基于深度学习技术，提出了一种面向电力系统的多粒度隐患检测方法MGNet。通过引入图像的多粒度信息，构建全局和局部网络，进行多粒度级检测；并通过不同粒度级检测结果的协作式融合，增强检测的全面性。在杆塔连接金具隐患和线路通道机械隐患2个数据集上进行了实验比较和分析，对所提模型的检测性能进行评估。通过与现有最优隐患检测基准方法相比，所提方法在2种不同数据集上的平均精度均值分别提升了2.74%和2.77%，验证了模型的有效性。
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| 关 键 词： | 隐患检测 多粒度信息 协作式融合 深度学习 电力系统 |
| 收稿时间： | 2020-09-02 |
Multi-granularity hazard detection method for electrical power system |
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| XU Xiaohua,QIAN Ping,WANG Yida,ZHOU Xinyue,XU Hanlin,XU Libing. Multi-granularity hazard detection method for electrical power system[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 520-530. DOI: 10.13700/j.bh.1001-5965.2020.0491 |
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| Authors: | XU Xiaohua QIAN Ping WANG Yida ZHOU Xinyue XU Hanlin XU Libing |
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| Affiliation: | 1.State Grid Zhejiang Electric Power Company Hangzhou Power Supply Company, Hangzhou 310020, China2.State Grid Zhejiang Electric Power Company, Hangzhou 310007, China |
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| Abstract: | As the security hazard of the electrical power system can lead to serious economic damage and social impacts, the potential hazard detection has become an indispensable part for electrical power system. With the advances of artificial intelligence, intelligent deep learning based hazard detection methods for electrical power system have emerged. Although the existing methods have made promising progress, most of them only consider the global or local features of the image, which cannot thoroughly characterize the imageand accurately conduct the hazard detection in the context of the electrical power system especially for the complex outdoor background. In the light of this, in this paper, we present a multi-granularity hazard detection network MGNet for the electrical power system. To be specific, we explore the multi-granularity representation of images with both the global and local representation learning networks. Based on that, we conduct the hazard detection at different granularity levels and finally collaboratively fuse the detection results to fulfill the precise hazard detection. Extensive experiments on two real-world datasets of hazard(i.e., tower connection fitting hazard dataset and transmission line channel mechanical hazard dataset) demonstrate the superiority of the detection performance of the proposed model. In particular, the mean average precision is improved by 2.74% and 2.77% on two datasets, respectively, compared with the existing optimal hazard detection benchmark method. |
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