基于故障原因辨识与多源先验知识的冬季输电线路重合闸策略

薛汉; 王建; 李晨浩; 张智缘; 南东亮; 赵启

doi:10.19783/j.cnki.pspc.260063

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

基于故障原因辨识与多源先验知识的冬季输电线路重合闸策略

理论分析 | 更新时间：2026-07-19

- 基于故障原因辨识与多源先验知识的冬季输电线路重合闸策略
- A winter transmission line reclosing strategy based on fault cause identification and multi-source prior knowledge
- 电力系统保护与控制 2026年54卷第14期
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19783/j.cnki.pspc.260063
  中图分类号：
- 纸质出版：2026
- 稿件说明：
移动端阅览
薛汉, 王建, 李晨浩, 等. 基于故障原因辨识与多源先验知识的冬季输电线路重合闸策略[J]. 电力系统保护与控制, 2026,54(14).

XUE Han, WANG Jian, LI Chenhao, et al. A winter transmission line reclosing strategy based on fault cause identification and multi-source prior knowledge[J]. Power System Protection and Control, 2026, 54(14).
薛汉, 王建, 李晨浩, 等. 基于故障原因辨识与多源先验知识的冬季输电线路重合闸策略[J]. 电力系统保护与控制, 2026,54(14). DOI： 10.19783/j.cnki.pspc.260063.

XUE Han, WANG Jian, LI Chenhao, et al. A winter transmission line reclosing strategy based on fault cause identification and multi-source prior knowledge[J]. Power System Protection and Control, 2026, 54(14). DOI： 10.19783/j.cnki.pspc.260063.

摘要

针对冬季输电线路故障辨识不精细、重合闸决策存在盲目性的问题，提出了一种基于故障原因辨识与多源先验知识的输电线路分级重合闸策略。首先，构建了基于时频网络的故障暂态波形特征提取方法，融合天气信息辨识覆冰、风偏、山火、异物等冬季典型故障的原因及其概率。其次，采用贝塔-二项分布对历史重合闸先验信息进行参数估计，以表征其不确定性。然后，考虑故障原因辨识结果与重合闸成功率分布抽样值，通过贝叶斯条件概率模型计算出当前故障下的重合成功概率，进而依据不同运行场景设定阈值，形成分级重合闸决策机制。最后，通过实际线路案例验证表明，所提方法的故障原因辨识准确率达到95.80%。相比现有的冬季重合闸成功率52.96%，所提分级重合闸策略的决策正确率可达79.41%，重合闸成功准确率为82.52%，显著提高了重合闸成功率，降低了盲目重合对电网的冲击。

Abstract

Aiming at the problems of insufficient precision in fault identification and the lack of decision-making rationality in transmission line reclosing during winter

a hierarchical reclosing strategy based on fault cause identification and multi-source prior knowledge is proposed. First

a fault transient waveform feature extraction method based on time-frequency network (TFN) is constructed

and the causes and probabilities of typical winter faults such as icing

wind swing

wildfires

and foreign object interference are identified by integrating weather information. Second

the beta-binomial distribution is used to estimate the parameters of historical reclosing prior information

capturing its inherent uncertainty. Then

the fault cause identification results are combined with sampling values from the reclosing success probability distribution

and a Bayesian conditional probability model is used to calculate the reclosing success probability under current fault conditions. Based on this

thresholds are set for different operating scenarios

forming a hierarchical reclosing decision-making mechanism. Finally

case studies on actual transmission lines show that the fault cause identification accuracy of the proposed method reaches 95.80%. Compared with the existing reclosing success rate of 52.96%

the proposed hierarchical reclosing strategy achieves a decision accuracy of 79.41% and reclosing success accuracy of 82.52%

significantly improving the reclosing success rate and reducing the impact of blind reclosing on the power grid.

关键词

Keywords

references

LIU Y, DOU K, XIE X, et al. An adaptive single-phase auto-reclosing scheme based on the Hausdorff distance algorithm for transmission lines with shunt reactors[J]. Computers and Electrical Engineering, 2025, 122.

XIE X, HUANG Z, FAN X, et al. Adaptive single-phase auto-reclosing scheme based on the moving average filter-quadrature signal generator for transmission lines with shunt reactors[J]. Electric Power Systems Research, 2023, 223.

HUANG G, YAN B, WEN N, et al. Study on jump height of transmission lines after ice-shedding by reduced-scale modeling test[J]. Cold Regions Science and Technology, 2019, 165.

LIANG Y, REN Y, YU J, et al. Current trajectory image-based protection algorithm for transmission lines connected to MMC-HVDC stations using CA-CNN[J]. Protection and Control of Modern Power Systems, 2023, 8(1): 1-15.

LI P, ZHANG M, CAO W, et al. Failure probability analysis of lightening strike transmission line based on data mining[C] // 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), October 30-November 1, 2020, Wuhan, China: 4070-4075.

HE W, CHEN W, WANG Y, et al. Exploration on automatic identification algorithm of transmission line mountain fire based on image recognition technology[J]. International Journal of Thermofluids, 2023, 20.

CHEN L, SHI X, PENG B, et al. Dynamic simulation of power systems considering transmission lines icing and insulators flashover in extreme weather[J]. IEEE Access, 2022, 10: 39656-39664.

DUAN X, HUANG Q, CHEN Z, et al. A rapid warning method for wildfire occurrences along the transmission corridor driven by power system monitoring data[J]. Frontiers in Energy Research, 2022, 10.

WANG J, LIN F, NAN D, et al. Transmission line fault identification method based on weighted federated learning and multimodal residual network[J]. International Journal of Electrical Power Energy Systems, 2025, 170.

CHEN Q, DONG X, TU G, et al. TFN: an interpretable neural network with time-frequency transform embedded for intelligent fault diagnosis[J]. Mechanical Systems and Signal Processing, 2024, 207.

XIE X, TIAN Y, WEI G. Deduction of sudden rainstorm scenarios: integrating decision makers’ emotions, dynamic Bayesian network and DS evidence theory[J]. Natural Hazards, 2023, 116(3): 2935-2955.

TONG Z, XU P, DENOEUX T. An evidential classifier based on Dempster-Shafer theory and deep learning[J]. Neurocomputing, 2021, 450: 275-293.

WU Y, ZHAO S, XING Z, et al. Detection of foreign objects intrusion into transmission lines using diverse generation model[J]. IEEE Transactions on Power Delivery, 2023, 38(5): 3551-3560.

XIONG X, WENG S, WANG J. An online early-warning method for wind swing discharge of the conductor toward the tangent tower and jumper toward the strain tower[J]. IEEE Transactions on Power Delivery, 2014, 30(1): 114-121.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于多模态残差网络融合波形与天气信息的输电线路故障原因辨识方法

考虑融冰因素的输电线路覆冰故障概率计算

基于 mRMR-GSWOA-XGBoost 的输电线路覆冰等级预测方法

同塔不换位线路的不对称度对差动保护的影响分析

基于元学习与改进 Transformer 的 N-k 小样本暂态稳定事故筛选方法