基于仿真分析的輸電線路樹木超高放電特性研究
石可頌,徐菲
(國網冀北電力有限公司廊坊供電公司,河北 廊坊 065000)
摘 要:架空輸電線路常因樹木超高生長造成線路故障跳閘,影響輸電線路的供電穩定性。架空輸電線路樹木超高生長會導致電暈放電—隱患放電—閃絡放電三個循序漸進的過程,基于樹木超高生長中放電過程,搭建 Ansys 仿真模型,研究了不同電壓等級的樹木超高放電過程凈空距離,同時開展樹木超高生長試驗,對試驗過程中樹木放電特性行波數據進行分析,獲取了不同凈空距離下行波特征,并針對行波特征制定樹木超高生長預警方法,為架空線路隱患放電提供了科學合理的預警方法,保證了架空輸電線路供電的穩定性。
關鍵詞: 架空輸電線路;Ansys 仿真;樹木放電;行波
中圖分類號:TM726.3 文獻標識碼:A 文章編號:1007-3175(2025)09-0034-08
Research on the Characteristics of Ultra-High Discharge of Trees on
Transmission Lines Based on Simulation Analysis
SHI Ke-song, XU Fei
(State Grid Jibei Electric Power Co., Ltd. Langfang Power Supply Company, Langfang 065000, China)
Abstract: Overhead transmission lines often trip due to the excessive growth of trees, which affects the power supply stability of the transmission lines. The ultra-high growth of trees in overhead transmission lines lead to three progressive processes: corona discharge, hazard discharge and flashover discharge. Based on the discharge process during the ultra-high growth of trees, this paper builds an Ansys simulation model to study the clearance distance of the ultra-high discharge process of trees at different voltage levels, meanwhile the tree ultra-high growth experiment is carried out, then the traveling wave data of tree discharge characteristics are analyzed during the experiment, thus obtaining traveling wave characteristics at different clearance distances to formulate the tree ultra-high growth early warning method, which provided a scientific and reasonable early warning method for hidden discharge of overhead lines and ensured the stability of power supply of overhead transmission lines.
Key words: overhead transmission line; Ansys simulation; tree discharge; traveling wave
參考文獻
[1] 陳良琴,唐海城,肖新華,等. 基于深度學習的輸電線路風險預警識別研究[J] . 電力大數據,2018,21(12) :1-5.
[2] 周小紅,李向歡,石蕾,等. 無人機傾斜攝影技術在電力巡線樹障檢測中的實踐應用研究[J]. 貴州電力技術,2019,22(8) :53-59.
[3] 金偉龍,周美英. 基于不同 BP 網絡層數的雙目立體視覺標定研究[J]. 光學技術,2015,41(1) :72-76.
[4] 樊高輝,劉尚合,魏明,等. 基于神經網絡曲線擬合的電暈電流數學模型研究[J] . 高電壓技術,2015,41(3) :1034-1041.
[5] 宋輝,代杰杰,張衛東,等. 復雜數據源下基于深度卷積網絡的局部放電模式識別[J] . 高電壓技術,2018,44(11) :3625-3633.
[6] 王小匆,劉亞東,盛戈皞,等. 基于改進 BPSO 算法的 PMU 優化配置新方法[J]. 廣東電力,2018,31(1) :62-67.
[7] 劉毓, 陸佳政, 羅晶, 等. 架空輸電線路山火同步衛星廣域監測與桿塔定位[J] . 電網技術,2018,42(4) :1322-1327.
[8] 張燕,杜紅樂. 基于異構距離的集成分類算法研究[J].智能系統學報,2019,14(4) :733-742.
[9] 朱付保,謝利杰,湯萌萌,等. 基于模糊 C-Means 的改進型 KNN 分類算法[J]. 華中師范大學學報(自然科學版),2017,51(6) :754-759.
[10] 王蕾,焦明海,代勇,等. 群體主動學習算法的移動電力交易行為研究[J] . 控制工程,2019,26(3) :484-491.