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高壓輸電線路多無人機自主協(xié)同巡線設(shè)計與測試

發(fā)布時間:2018-02-12 14:40

  本文關(guān)鍵詞: 多旋翼無人機 高壓線巡線 多無人機協(xié)同 機器學習 一致性 出處:《電力系統(tǒng)自動化》2017年10期  論文類型:期刊論文


【摘要】:針對目前巡檢高壓輸電線路的固定翼無人機不能精細化巡查,而多旋翼無人機運動速度慢且需要人工遙控操作導致安全風險較大的問題,提出多架多旋翼無人機自主協(xié)同精細化巡線方案。探討了利用兩架部署在導線兩側(cè)的無人機通過機間通信協(xié)同巡檢高壓輸電桿塔和導線的方法,并采用機器學習技術(shù)和多智能體一致性控制算法設(shè)計無人機自主飛行控制器。根據(jù)設(shè)計結(jié)果開發(fā)一站四機系統(tǒng)驗證巡線方案,實驗部署兩機為一組,兩組并行作業(yè)。結(jié)果表明該系統(tǒng)按預期完成高壓輸電線路的巡檢作業(yè),未發(fā)生誤報缺陷,相比單個遙控操作的多旋翼無人機巡檢方案,多機協(xié)同方案無需人工干預飛行,總巡線速度超過單機最高時速,雙機協(xié)同對單個桿塔的平均巡檢時間小于遙控單機巡檢時間的一半。
[Abstract]:In view of the problem that the fixed-wing UAV which is currently inspecting HV transmission lines can not be meticulously inspected and the multi-rotor UAV is moving slowly and needs manual remote control operation, it has a high safety risk. This paper presents a scheme of autonomous cooperative and fine line patrol for multiple multi-rotor UAVs, and discusses the method of using two UAVs deployed on both sides of the wire to patrol and inspect the high voltage transmission towers and conductors by means of inter-aircraft communication. The autonomous flight controller of UAV is designed by means of machine learning technology and multi-agent consistency control algorithm. According to the design results, the scheme of one station and four aircraft system is developed to verify the inspection line, and two aircraft are deployed as a group. The results show that the system completes the patrol inspection of HV transmission line as expected, and there is no false alarm defect. Compared with the multi-rotor UAV patrol scheme operated by single remote control, the multi-aircraft cooperative scheme does not need manual intervention flight. The total patrol speed exceeds the maximum speed of the single machine, and the average inspection time for a single tower with two machines is less than half of that of the remote control.
【作者單位】: 電子科技大學航空航天學院;國網(wǎng)四川省電力公司攀枝花供電公司;國網(wǎng)四川省電力公司電力科學研究院;
【基金】:高等學校博士學科點專項科研基金資助項目(20130185110023)~~
【分類號】:TM75

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1 孔祥慧;基于機器學習的變電站數(shù)據(jù)檢測技術(shù)研究[D];華北電力大學;2016年

2 陳金楷;基于機器學習的鍋爐主輔機狀態(tài)監(jiān)測研究[D];華中科技大學;2015年

3 王琦;機器學習研究及在風力預測中的應(yīng)用[D];復旦大學;2012年

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