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基于機(jī)器學(xué)習(xí)的緊湊型架空線路脫冰跳躍風(fēng)險(xiǎn)預(yù)測研究

來源：電工電氣發(fā)布時(shí)間：2025-07-24 11:24 瀏覽次數(shù)：70

基于機(jī)器學(xué)習(xí)的緊湊型架空線路脫冰跳躍風(fēng)險(xiǎn)預(yù)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(wǎng)有限責(zé)任公司廣州供電局，廣東廣州 510640；

2 國網(wǎng)湖南省電力有限公司技術(shù)技能培訓(xùn)中心，湖南長沙 410131；

3 長沙電力職業(yè)技術(shù)學(xué)院供電服務(wù)系，湖南長沙 410131；

4 貴州電網(wǎng)有限責(zé)任公司電力科學(xué)研究院，貴州貴陽 550000)

摘要：架空線路發(fā)生脫冰跳躍會導(dǎo)致線間間距減小，甚至?xí)l(fā)生放電事故，影響電網(wǎng)的安全運(yùn)行。對基于機(jī)器學(xué)習(xí)的緊湊型架空線路脫冰跳躍風(fēng)險(xiǎn)預(yù)測進(jìn)行研究，提出了一種基于最大跳躍幅值的脫冰線路放電風(fēng)險(xiǎn)評估流程。以中國南方地區(qū)某500 kV緊湊型架空線路為例，利用數(shù)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(gòu)建樣本數(shù)據(jù)集；建立基于 BP 神經(jīng)網(wǎng)絡(luò)的脫冰線路最大跳躍幅值預(yù)測模型，將線路覆冰厚度、脫冰率、檔距、高差、導(dǎo)線截面積作為輸入，最大跳躍幅值作為輸出，通過機(jī)器學(xué)習(xí)實(shí)現(xiàn)對脫冰線路最大跳躍幅值的預(yù)測，并結(jié)合所提風(fēng)險(xiǎn)評估流程，進(jìn)一步實(shí)現(xiàn)對脫冰線路放電風(fēng)險(xiǎn)的預(yù)測。基于該緊湊型線路實(shí)際放電案例，驗(yàn)證所提預(yù)測模型的準(zhǔn)確性，結(jié)果表明：預(yù)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(yù)測和仿真條件下對應(yīng)脫冰線路的風(fēng)險(xiǎn)狀態(tài)一致；預(yù)測風(fēng)險(xiǎn)結(jié)果與實(shí)際脫冰線路放電案例結(jié)果吻合，說明了所提風(fēng)險(xiǎn)預(yù)測方法可以方便快捷地實(shí)現(xiàn)脫冰線路風(fēng)險(xiǎn)的預(yù)測。

關(guān)鍵詞： 覆冰；脫冰跳躍；風(fēng)險(xiǎn)預(yù)測；風(fēng)險(xiǎn)評估；緊湊型架空線路

中圖分類號：TM726.3 文獻(xiàn)標(biāo)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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基于機(jī)器學(xué)習(xí)的緊湊型架空線路脫冰跳躍風(fēng)險(xiǎn)預(yù)測研究