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基于相似度比较的环状柔直配电网线路单极接地故障保护 被引量:17

Similarity Comparison Based Single-pole Grounding Fault Protection for Lines in Ring-shaped Flexible DC Distribution Network
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摘要 直流配电网线路发生单极故障的概率较高,同时受过渡电阻变化范围较大的影响,故障识别难度大。首先,针对环状直流配电网的中压直流线路单极接地故障的保护问题,分析了单极接地故障后系统的零模网络。基于系统的零模网络,分别建立了区内、区外故障的数学模型,并通过Hausdorff距离算法对任意时刻零模差动电流采样信号和由区内、区外模型求得的零模差动电流值分别进行相似度比较,实现故障的快速识别和隔离,保护定值整定简单。最后,在PSCAD/EMTDC中搭建仿真模型,验证了所提保护方案的可行性,结果表明该方案识别准确度高、耐受过渡电阻能力强。 The probability of single-pole grounding faults on lines of DC distribution network is high. It is difficult to identify the fault due to the large variation range of the transition resistance. With regard to the protection against single-pole grounding faults of medium-voltage DC lines in ring-shaped DC distribution networks, this paper analyzes the zero-mode network of the system after single-pole grounding fault. Based on the zero-mode network, the mathematical models of internal-fault and external-fault are established. Then the Hausdorff distance algorithm is employed to compare the similarity between the sampled zero-mode differential current and the calculated value obtained by the internal-fault and external-fault models, which achieves rapid identification and isolation of the fault. It is simple to set the relay threshold as well. Finally, the feasibility of the protection scheme is verified via a simulation model in PSCAD/EMTDC. The results show that the proposed scheme has high fault identification accuracy and tolerance to transition resistance.
作者 戴志辉 刘雪燕 黄敏 鲁浩 刘媛 DAI Zhihui;LIU Xueyan;HUANG Min;LU Hao;LIU Yuan(School of Electrical and Electronic Engineering,North China Electric Power University,Baoding 071003,China;Electric Power Research Institute of State Grid Shandong Electric Power Company,Jinan 250061,China)
出处 《电力系统自动化》 EI CSCD 北大核心 2019年第23期107-115,共9页 Automation of Electric Power Systems
基金 国家自然科学基金资助项目(51877084) 河北省自然科学基金资助项目(E2018502063) 中央高校基本科研业务费专项资金资助项目(2017MS096)~~
关键词 直流配电网 单极接地故障 零模网络 模型相似度 故障识别 DC distribution network single-pole grounding fault zero-mode network model similarity fault identification
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