A winter transmission line reclosing strategy based on fault cause identification and multi-source prior knowledge

XUE Han; WANG Jian; LI Chenhao; ZHANG Zhiyuan; NAN Dongliang; ZHAO Qi

doi:10.19783/j.cnki.pspc.260063

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A winter transmission line reclosing strategy based on fault cause identification and multi-source prior knowledge

更新时间：2026-07-19

- A winter transmission line reclosing strategy based on fault cause identification and multi-source prior knowledge
- Power System Protection and Control Vol. 54, Issue 14, (2026)
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- DOI：10.19783/j.cnki.pspc.260063
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- Published：2026
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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：

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.

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references

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Related Institution

Electric Power Research Institute of State Grid Xinjiang Electric Power Co., Ltd.

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