基于鸟类行为特征与电场传感的输电线路防鸟筑巢行为预警方法研究

买亚龙; 郑建锋; 陈俊; 张雨欣; 吴杰; 毛庆萌; 邢雁凯; 李坚

买亚龙,郑建锋,陈俊,张雨欣,吴杰,毛庆萌,邢雁凯,李坚.基于鸟类行为特征与电场传感的输电线路防鸟筑巢行为预警方法研究[J].电测与仪表,2026,63(7):15-27.
MAI Yalong,ZHENG Jianfeng,CHEN Jun,ZHANG Yuxin,WU Jie,MAO Qingmeng,XING Yankai,LI Jian.Research on an early warning method for preventing bird nesting on transmission lines based on bird behavior characteristics and electric field sensing[J].Electrical Measurement & Instrumentation,2026,63(7):15-27.

基于鸟类行为特征与电场传感的输电线路防鸟筑巢行为预警方法研究

Research on an early warning method for preventing bird nesting on transmission lines based on bird behavior characteristics and electric field sensing

DOI：10.19753/ j.issn1001-1390.2026.07.002

中文关键词: 监测与预警鸟类习性电场特性输配电设备鸟害故障

英文关键词:monitoring and early warning, bird habits, electric field characteristics, power transmission and distribution equipment, bird-related faults

基金项目:国家自然科学基金资助项目（51977025）

作者中文名	作者英文名	单位
买亚龙	MAI Yalong	电子科技大学机械与电气工程学院, 成都 611731
郑建锋	ZHENG Jianfeng	国网浙江省电力有限公司衢州供电公司, 浙江衢州 324000
陈俊	CHEN Jun	国网浙江省电力有限公司衢州供电公司, 浙江衢州 324000
张雨欣	ZHANG Yuxin	电子科技大学机械与电气工程学院, 成都 611731
吴杰	WU Jie	电子科技大学长三角研究院(衢州), 浙江衢州 324004
毛庆萌	MAO Qingmeng	电子科技大学机械与电气工程学院, 成都 611731
邢雁凯	XING Yankai	电子科技大学机械与电气工程学院, 成都 611731
李坚	LI Jian	电子科技大学机械与电气工程学院, 成都 611731

摘要点击次数: 168

中文摘要:

针对鸟类行为引发的输电线路故障问题,文章旨在解决现有监测技术在恶劣环境下适应性差、响应延迟的短板,提出一种融合鸟类行为特征与电场传感的实时预警方法。解析鸟类行为的多维异质性特征,建立物种-环境-时空的关联规则;基于行为规律构建确定性预测模型,依托历史巢穴数据实现杆塔风险分级定位,平均预测准确度达93.01%以上,实现精准防治区域预测,为监测装置的重点部署提供理论指导。进一步地,针对传统监测装置在复杂条件下适应差问题,利用电磁场不受环境干扰的特性,创新性地提出了一种基于电场传感的鸟类入侵杆塔的监测装置及算法。建立了两个测量点间的压差与空间电场强度的映射关系,并以此为基础开发了基于空间电场畸变特性的鸟类入侵监测算法,实现对鸟群、鸟窝的准确辨识。最后研发了一种差分式双探针电场传感装置,并在实验室搭建的等比模型上开展了实验验证。结果表明,所提方法能够实现对鸟窝位置的定位以及鸟类活动的监测,为电网鸟害防治提供了高精度、强抗干扰的主动预警解决方案。

英文摘要:

Regarding the transmission line faults caused by bird behaviors, this study aims to address the shortcomings of existing monitoring technologies in their poor adaptability and delayed response in harsh environments, and proposes a real-time early warning method that integrates bird behavior characteristics with electric field sensing. Firstly, the multi-dimensional heterogeneity characteristics of bird behaviors are analyzed, and the association rules of species, environment, and time-space are established. Secondly, a deterministic prediction model is constructed based on behavioral patterns, and risk classification and localization of transmission towers are achieved relying on historical nest data, with an average prediction accuracy is above 93. 01%. This enables accurate prediction of prevention and control areas, providing theoretical guidance for the focused deployment of monitoring devices. Furthermore, aiming at the problem of poor adaptability of traditional monitoring devices under complex conditions, this paper innovatively proposes a monitoring device and algorithm for bird intrusion on transmission towers based on electric field sensing by utilizing the characteristic that electromagnetic fields are not affected by environmental interference. The mapping relationship between the voltage difference between two measurement points and the spatial electric field intensity is established, and on this basis, a bird intrusion monitoring algorithm based on spatial electric field distortion characteristics is developed to achieve accurate identification of bird flocks and bird nests. Finally, a differential dual-probe electric field sensing device is developed, and experimental verification is carried out on a scaled model built in the laboratory. The results show that the proposed method can achieve localization of bird nest positions and detection of bird activities, providing a high-precision, strong anti-interference active early warning solution for bird damage prevention in power grids.

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