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作 者:王桥梅 吴浩 杨杰 李栋 刘益岑 WANG Qiaomei;WU Hao;YANG Jie;LI Dong;LIU Yicen(School of Automation and Information Engineering,Sichuan University of Science&Engineering,Yibin 644000,China;Artificial Intelligence Key Laboratory of Sichuan Province,Yibin 644000,China;State Grid Sichuan Electric Power Company Electric Power Research Institute,Chengdu 610000,China)
机构地区:[1]四川轻化工大学自动化与信息工程学院,四川宜宾644000 [2]人工智能四川省重点实验室,四川宜宾644000 [3]国网四川省电力公司电力科学研究院,成都610000
出 处:《四川轻化工大学学报(自然科学版)》2022年第4期75-84,共10页Journal of Sichuan University of Science & Engineering(Natural Science Edition)
基 金:四川省科技厅项目(2019YJ0477,2018JY0386,2020YFG0178);自贡市科技局项目(2019YYJC13,2019YYJC02,2020YGJC16);四川轻化工大学研究生创新基金项目(y2019012)。
摘 要:针对现有行波保护存在耐受过渡电阻能力低、对保护装置采样频率要求高等问题,提出一种基于曼哈顿距离的高压直流(HVDC)输电线路故障识别的新方法。对HVDC输电线路发生区内外故障时的行波传播特征进行分析发现,区外故障时整流侧和逆变侧的电流行波相似度较高,而区内故障时两侧的电流行波相似度较低,因此本文引入曼哈顿距离来衡量两侧波形的相似度,提出一种基于曼哈顿距离的HVDC输电线路保护判据。仿真结果表明,该保护的判据简单,基本不受故障类型和故障距离等因素的影响,具有较强的耐受过渡电阻能力和抗干扰能力。Aiming at the problems of poor tolerance to transition resistance and high sampling frequency of protection devices,a new method of fault identification for high voltage direct current(HVDC)transmission lines based on Manhattan distance has been proposed in the present study.With analyzing the characteristics of the traveling wave distribution of HVDC transmission lines during internal and external faults,it is found that the similarity of the current traveling waves on the rectifier side and the inverter side is higher when the external fault occurs,while the similarity of the current traveling waves on both sides is low when the internal fault occurs.Therefore,the Manhattan distance is introduced to measure the similarity of the waveforms on both sides,and a protection criterion for HVDC transmission lines based on the Manhattan distance is proposed.Simulation results show that the protection criterion is simple,basically not affected by factors such as fault type and fault distance,exhibiting strong resistance to transition resistance and strong anti-noise ability.
关 键 词:高压直流 输电线路 S变换 曼哈顿距离 故障识别
分 类 号:TM773[电气工程—电力系统及自动化]
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