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      |本期目錄/Table of Contents|

      [1]焦在濱,宋新堯,李炳緒,等.一種采用2級反向傳播神經網絡的輸電線路故障測距方法[J].西安交通大學學報,2019,53(09):61-69.[doi:10.7652/xjtuxb201909009]
       JIAO Zaibin,SONG Xinyao,LI Bingxu,et al.A Fault Location Method for Transmission Line Based on Two-Level Back Propagation Neural Network[J].Journal of Xi'an Jiaotong University,2019,53(09):61-69.[doi:10.7652/xjtuxb201909009]
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      一種采用2級反向傳播神經網絡的輸電線路故障測距方法(PDF)

      《西安交通大學學報》[ISSN:0253-987X/CN:61-1069/T]

      卷:
      53
      期數:
      2019年第09期
      頁碼:
      61-69
      欄目:
      出版日期:
      2019-09-10

      文章信息/Info

      Title:
      A Fault Location Method for Transmission Line Based on Two-Level Back Propagation Neural Network
      作者:
      焦在濱12 宋新堯12 李炳緒1 吳潤東3
      1.西安交通大學電氣工程學院, 710049, 西安; 2.西安交通大學陜西省智能電網重點實驗室, 710049, 西安; 3.國網安徽省電力有限公司經濟技術研究院, 230022, 合肥
      Author(s):
      JIAO Zaibin12 SONG Xinyao12 LI Bingxu1 WU Rundong3
      1.School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China; 2.Shaanxi Key Laboratory of Smart Grid, Xi'an Jiaotong University, Xi'an 710049, China; 3.Economic and Technological Research Institute, State Grid Anhui Electric Power Co.Ltd., Hefei 230022, China
      關鍵詞:
      輸電線路 故障測距 2級反向傳播神經網絡 過渡電阻 測量誤差
      Keywords:
      transmission line fault location two-level back propagation neural network transition resistance measurement error
      分類號:
      TM773
      DOI:
      10.7652/xjtuxb201909009
      摘要:
      針對輸電線路的故障測距中過渡電阻及電氣量測量誤差影響測距精度的問題,提出了一種采用2級反向傳播(BP)神經網絡的輸電線路故障測距方法。通過分析雙端電氣量隨過渡電阻的變化情況,確定了雙端電氣量變化規律的區域特性,提出了基于過渡電阻分區后在不同區域分別進行精確定位的研究思路。利用第1級網絡對雙端電氣量進行數據融合,將故障場景分為低阻故障和高阻故障,再利用第2級網絡中的低阻故障測距網絡和高阻故障測距網絡分別對低阻故障和高阻故障的雙端電氣量進行數據融合,計算出精確的故障位置。對訓練方法進行改進,在測量電氣量中加入高斯白噪聲信號來模擬含互感器誤差的樣本,將無誤差樣本和含誤差樣本組成的重復樣本對作為訓練樣本,使訓練后的BP神經網絡對隨機測量誤差具備一定的適應能力。電磁暫態仿真結果表明:所提方法不受過渡電阻影響,在高阻故障情況下測距結果的最大誤差仍然低于1%,且對隨機誤差具有較好的適應性,在輸入電氣量存在一定測量誤差的情況下測距結果的最大誤差低于2.5%,具有良好的應用前景。
      Abstract:
      Aiming at the problem that transition resistance and measurement error affect the accuracy of fault location in transmission line, a novel method of fault location for transmission line based on two-level back propagation(BP)neural network is proposed.Analyzing the change of the two-terminal electrical quantity with transition resistance, the regional characteristics of the change regularity of the two-level electrical quantity are determined, and the research idea of precise positioning in different regions is introduced.The first level network is used to fuse the two-terminal electrical quantity, and fault scenarios are divided into low-resistance fault and high-resistance fault.Low-resistance fault location network and high-resistance fault location network in the second level network are used to fuse the two-terminal electrical quantity of low-resistance fault and high-resistance fault respectively, and the accurate fault location is evaluated.Then the training method is improved, white Gaussian noise is added to the measured electrical quantity to simulate the transformer error.The repetitive sample pairs composed of error-free sample and error-containing sample is taken as training sample, so that the trained BP neural network has certain adaptability to the random measurement error.Electromagnetic transient simulation results show that the proposed method is not affected by transition resistance, and the maximum error of fault location is still less than 1% under high-resistance faults.In addition, the method has good adaptability to random errors, and the maximum error of fault location is less than 2% when there are certain measurement errors in the input electrical quantity.

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      備注/Memo

      備注/Memo:
      收稿日期: 2019-03-14。作者簡介: 焦在濱(1976—),男,副教授,博士生導師;痦椖: 國家重點研發計劃資助項目(2017YFB0902900)。
      更新日期/Last Update: 2019-09-04
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