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| Health status assessment and remaining life prediction of transmission lines based on multi-factor coupling |
| LI Zhiwei1, WU Yue2, WANG Wei1, WANG Can3, ZHANG Liang3 |
1. State Grid Hubei Economic and Technological Research Institute, Wuhan 430200; 2. Hubei Huazhong Electric Power Technology Development Co., Ltd, Wuhan 430200; 3. State Grid Wuhan Power Supply Company, Wuhan 430200 |
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Abstract Equipment condition monitoring is a core aspect of power system operation and maintenance management. Through dynamic monitoring and systematic analysis of equipment operating conditions, potential risks and hazards can be proactively identified. The remaining life of transmission lines, as a time-scale parameter indicating their sustained safe service capability under current health conditions, is scientifically predicted as a key basis for equipment retirement and renewal, as well as optimization of maintenance cycles. It is of great significance for precise allocation and efficient utilization of operation and maintenance resources. In view of existing research that has not effectively integrated transmission line health assessment, remaining life, operation and maintenance decision- making, resulting in insufficient multi-factor coupling dimensions, this paper constructs a multi-factor coupled transmission line health assessment index system, quantifies the health status of transmission line units and the overall system using fuzzy membership functions, introduces defect coefficients and pollution coefficients to correct aging models, and establishes a remaining life prediction model. Taking a 220 kV transmission line of a power grid enterprise as a case study, health condition assessment and remaining life prediction are carried out, which reflects the inherent correlation between the current health status and remaining life of transmission lines, indicating that the proposed method can provide reliable quantitative support for differentiated operation and maintenance of transmission lines, optimization of transformation sequence, scope and scheme formulation, and enhances the level of refined operation and maintenance of equipment assets for power grid enterprises effectively.
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Received: 09 January 2026
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| Cite this article: |
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LI Zhiwei,WU Yue,WANG Wei等. Health status assessment and remaining life prediction of transmission lines based on multi-factor coupling[J]. Electrical Engineering, 2026, 27(9): 41-49.
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| URL: |
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https://dqjs.cesmedia.cn/EN/Y2026/V27/I9/41
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[1] 魏茳彬. 基于数据挖掘的输电线路运行状态评估方法[J]. 电气技术与经济, 2025(6): 396-398. [2] 刘柏延, 张萌萌, 相静, 等. 基于老化预防的输电线路状态检修方法研究[J]. 电气技术, 2025, 26(4): 73-79. [3] 张煜恒, 沙池橙, 廖海林, 等. 基于杆件核心度的输电铁塔健康状态可靠度评价研究[J]. 西安理工大学学报, 2023, 39(2): 281-290. [4] 纪航, 袁奇, 蒋文贤, 等. 基于动态权变层次分析法的输电网络线路风险评估模型[J]. 电气应用, 2015, 34(增刊1): 452-457. [5] 李银峰, 王晗一好. 电力输电线路的状态评估与风险对策[J]. 集成电路应用, 2020, 37(12): 106-107. [6] 李旋, 陈颢元, 郭耀杰. 基于正常使用极限状态的杆塔剩余使用寿命研究[J]. 武汉大学学报(工学版), 2017, 50(增刊1): 342-346. [7] 马维贵. 面向老旧线路的剩余寿命评估研究[J]. 电工技术, 2018(23): 1-3. [8] 叶建锋, 邓德发, 张明, 等. 某输电线路地线腐蚀情况分析及寿命评估[J]. 湖北电力, 2020, 44(2): 47-51, 106. [9] 赵昌东, 项石虎, 王尧. 基于模型融合的电子元器件个体剩余寿命预测方法[J]. 电工技术学报, 2023, 38(18): 4978-4993. [10] 宋欣杰, 金一鸣. 基于深度学习的架空输电线路绝缘子识别方法研究[J]. 电气技术, 2025, 26(9): 62-68, 78. [11] 张烨, 李博涛, 尚景浩, 等. 基于多尺度卷积注意力机制的输电线路防振锤缺陷检测[J]. 电工技术学报, 2024, 39(11): 3522-3537. [12] 唐玉涛, 束洪春, 刘皓铭, 等. 基于CABFAM- Transformer的输电线路在线测距实测行波预分类方法[J]. 电工技术学报, 2025, 40(5): 1455-1470. [13] 陈洋. 影响输电线路安全性的因素分析[J]. 电气技术, 2019, 20(增刊1): 90-92, 98. [14] 董志涵. 输电线路损耗分析与节能改造经济效益评估模型的构建与应用[J]. 自动化应用, 2025, 66(增刊1): 85-87. [15] 徐岩, 迟成, 石海勇. 基于LCC的输电线路发展改造方案选择[J]. 华北电力大学学报(自然科学版), 2015, 42(5): 33-37. [16] 孟毅, 景威. 输电线路增容改造对三相电压不平衡度的影响[J]. 光源与照明, 2024(9): 183-185. [17] 张东军. 老旧输电线路改造及措施研究[J]. 科技与创新, 2015(18): 148. [18] Liu X, Wirtz K W.Consensus building in oil spill response planning using a fuzzy comprehensive evaluation[J]. Coastal Management, 2007, 35(2/3): 195-210. [19] DL/T1249—2013 架空输电线路运行状态评估技术导则[S]. [20] 孙东磊, 杨思, 许易经, 等. 电网设备状态检修的时变决策模型[J]. 电力系统及其自动化学报, 2021, 33(6): 100-109. |
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