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基于機器學習的緊湊型架空線路脫冰跳躍風險預測研究

來源：電工電氣發布時間：2025-07-24 11:24 瀏覽次數：109

基于機器學習的緊湊型架空線路脫冰跳躍風險預測研究

陳易飛¹，王睿君¹，張宇¹，杜樂¹，林杏¹，莫姝¹，劉子其^{2, 3}，黃歡⁴

(1 廣東電網有限責任公司廣州供電局，廣東廣州 510640；

2 國網湖南省電力有限公司技術技能培訓中心，湖南長沙 410131；

3 長沙電力職業技術學院供電服務系，湖南長沙 410131；

4 貴州電網有限責任公司電力科學研究院，貴州貴陽 550000)

摘要：架空線路發生脫冰跳躍會導致線間間距減小，甚至會發生放電事故，影響電網的安全運行。對基于機器學習的緊湊型架空線路脫冰跳躍風險預測進行研究，提出了一種基于最大跳躍幅值的脫冰線路放電風險評估流程。以中國南方地區某500 kV緊湊型架空線路為例，利用數值模擬方法得到不同仿真工況下架空線路的最大跳躍幅值，構建樣本數據集；建立基于 BP 神經網絡的脫冰線路最大跳躍幅值預測模型，將線路覆冰厚度、脫冰率、檔距、高差、導線截面積作為輸入，最大跳躍幅值作為輸出，通過機器學習實現對脫冰線路最大跳躍幅值的預測，并結合所提風險評估流程，進一步實現對脫冰線路放電風險的預測。基于該緊湊型線路實際放電案例，驗證所提預測模型的準確性，結果表明：預測和仿真得到的最大跳躍幅值的絕對誤差不超過0.3 m，且預測和仿真條件下對應脫冰線路的風險狀態一致；預測風險結果與實際脫冰線路放電案例結果吻合，說明了所提風險預測方法可以方便快捷地實現脫冰線路風險的預測。

關鍵詞： 覆冰；脫冰跳躍；風險預測；風險評估；緊湊型架空線路

中圖分類號：TM726.3 文獻標識碼：A 文章編號：1007-3175(2025)07-0060-08

Study on Risk Prediction of Ice-Shedding Jump for Compact Overhead

Lines Based on Machine Learning

CHEN Yi-fei¹, WANG Rui-jun¹, ZHANG Yu¹, DU Le¹, LIN Xing¹, MO Shu¹, LIU Zi-qi^{2, 3}, HUANG Huan⁴

（1 Guangdong Power Grid Co., Ltd. Guangzhou Power Supply Bureau, Guangzhou 510640, China;

2 Technical Skills Training Center of State Grid Hunan Electric Power Co., Ltd, Changsha 410131, China;

3 Power Supply Service Department, Changsha Electric Power Technical College, Changsha 410131, China;

4 Guizhou Power Grid Co., Ltd. Electric Power Science Research Institute, Guiyang 550000, China）

Abstract: The occurrence of ice-shedding jumps on overhead lines can lead to a reduction in the spacing between lines, and even discharge accidents can occur, affecting the safe operation of the power grid. In this paper, machine learning-based risk prediction of ice-shedding jumps for compact overhead lines is investigated, and a process for assessing the risk of ice-shedding lines discharges based on the maximum jump amplitude is proposed. Taking a 500 kV compact overhead line in southern China as an example, the maximum jump amplitude of the overhead line under different simulation conditions was obtained by numerical simulation method, and the sample dataset was constructed. A prediction model of the maximum jump amplitude of the ice-shedding line based on BP neural network was established, and the ice thickness, ice-shedding rate,pitch, height difference, and cross-sectional area of the wire were taken as inputs, and the maximum jump amplitude was used as the output, and the maximum jump amplitude of the ice-shedding line was predicted through machine learning, and the risk of discharge risk of the ice-shedding line was further predicted by combining the proposed risk assessment process. Based on the actual discharge case of the compact line, the accuracy of the proposed prediction model is verified, and the results show that the absolute error of the maximum jump amplitude obtained by the prediction and simulation is not more than 0.3 m, and the risk state of the corresponding ice-shedding line is consistent under the prediction and simulation conditions. The prediction risk results are consistent with the actual ice-shedding line discharge case results, which indicates that the proposed risk prediction method can easily and quickly realize the risk prediction of ice-shedding lines.

Key words: icing; ice-shedding jump; risk prediction; risk assessment; compact overhead line

參考文獻

[1] 于幼文，金永純，高毅. 我國 500 kV 緊湊型輸電線路的研究與應用[J]. 電力設備，2004，5(6) ： 9-12.

[2] 習近平向《聯合國氣候變化框架公約》第二十六次締約方大會世界領導人峰會發表書面致辭[N] . 人民日報，2021-11-02(1)．

[3] 張行，王逸飛，何迪，等. 電網防災減災現狀分析及建議[J]. 電網技術，2016，40(9) ：2838-2844.

[4] YANG Lin, CHEN Yifei, MEI Lulu, et al.Prediction Method for Response Characteristics Parameters of Isolated-Span Overhead Lines after Ice-Shedding Based on Finite Element Simulation and Machine Learning[J].Electric Power Systems Research，2024，229 ：110141.

[5] YANG L, CHEN Y F, HAO Y P, et al.Detection Method for Equivalent Ice Thickness of 500 kV Overhead Lines Based on Axial Tension Measurement and Its Application [J].IEEE Transactions Instrumentation Measurement，2023，72 ：1-11.

[6] 陳易飛，陽林，郝艷捧，等. 冰風荷載作用下架空線路脫冰跳躍風險評估方法[J] . 電力大數據，2024，27(1) ：1-9.

[7] MORGAN V T, SWIFT D A.Jump height of overheadline conductors after the sudden release of ice loads[J].Proceedings of the Institution of Electrical Engineers，1964，111(10) ：1736-1746.

[8] JAMALEDDING A, MCCLURE G, ROUSSELET J, et al.Simulation of ice-shedding on electrical transmission lines using ADINA[J].Computers and Structures，1993，47(4/5) ：523-536.

[9] VAN DYKE P, LANEVILLE A.Galloping of a single conductor covered with a D-section on a highvoltage overhead test line[J].Journal of Wind Engineering and Industrial Aerodynamics，2008，96(6/7) ：1141-1151.

[10] 陳勇，胡偉，王黎明，等. 覆冰導線脫冰跳躍特性研究[J]. 中國電機工程學報，2009，29(28) ：115-121.

[11] 謝獻忠，李丹，黃偉，等. 塔線體系脫冰跳躍動力特性實驗研究[J]. 應用力學學報，2017，34(5) ：855-861.

[12] HUANG Guizao, YAN Bo, WEN Nan, 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 ：102781.

[13] KOLLAR L E, FARZANEH M, VAN DYKE P.Modeling Ice Shedding Propagation on Transmission Lines with or without Interphase Spacers[J].IEEE Transactions on Power Delivery，2013，28(1) ：261-267.

[14] YAN Bo, CHEN Kequan, GUO Yueming, et al.Numerical simulation study on jump height of iced transmission lines after ice shedding[J].IEEE Transactions on Power Delivery，2013，28(1) ：216-225．

[15] WU Chuan, YAN Bo, ZHANG Liang, et.al.A method to calculate jump height of iced transmission lines after ice-shedding[J].Cold Regions Science and Technology，2016，125 ：40-47.

[16] 張殿生. 電力工程高低壓送電線路設計手冊[M]. 北京：中國電力出版社，2005.

[17] RADMER D T, KUNTZ P A, CHRISTIE R D, et al.Predicting vegetation-related failure rates for overhead distribution feeders[J].IEEE Transactions on Power Delivery，2002，17(4) ：1170-1175．

[18] 廖崢，熊小伏，李新，等. 基于 BP 神經網絡的輸電線路舞動預警方法[J] . 電力系統保護與控制，2017，45(19) ：154-161.

[19] WANG Jian, XIONG Xiaofu, ZHOU Ning, et al.Early warning method for transmission line galloping based on SVM and AdaBoost bi-level classfiers[J].IET Generation, Transmission &Distribution，2016，10(14) ：3499-3507.

[20] YUAN S, QUIRING S M, ZHU L, et al.Development of a typhoon power outage model in Guangdong,China[J].International Journal of Electrical Power & Energy Systems，2020，117 ：105711.

[21] 鄭凌銘，舒勝文，陳彬，等. 強臺風環境下基于格點化和支持向量機的 10 kV 桿塔受損量預測方法[J]. 高電壓技術，2020，46(1) ：42-50．

[22] 謝云云，薛禹勝，文福拴，等. 冰災對輸電線故障率影響的時空評估[J] . 電力系統自動化，2013，37(18) ：32-41.

[23] 謝云云，薛禹勝，王昊昊，等. 電網雷擊故障概率的時空在線預警[J] . 電力系統自動化，2013，37(17) ：44-51.

[24] 中國電力企業聯合會.110 kV～750 kV 架空輸電線路設計規范：GB 50545—2010[S] . 北京：人民出版社，2010 ：109.

[25] WEN Yi, CHEN Yifei, WU Jianrong, et al.Research on Risk Assessment and Suppression Measures for Ice-Shedding on 500 kV Compact Overhead Lines[J].Energies，2022，15(21) ：8005.

[26] 邵天曉. 架空送電線路的電線力學計算[M]. 北京：中國電力出版社，2003.

[27] 王斌，李立浧，魏發生，等. 不均勻覆冰下的架空地線覆冰在線監測力學計算模型改進[J] . 高電壓技術，2022，48(11) ：4538-4545.

[28] HAYKIN S.Neural Networks: A Comprehensive Foundation[M].New Jersey ：Prentice Hall PTR，1998.

[29] 陸益錳，安旭文，劉智勇，等. 輸電線路覆冰厚度的概率分布模型比較分析[J] . 四川建筑，2015，35(2) ：263-266.

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