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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：108

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

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