| 引用本文: | 高锋阳,李昭君,袁成,齐晓东,李晓峰,庄圣贤,李浩武.含特殊负荷的配电网故障定位与识别[J].电力自动化设备,2020,40(8): |
| GAO Fengyang,LI Zhaojun,YUAN Cheng,QI Xiaodong,LI Xiaofeng,ZHUANG Shengxian,LI Haowu.Fault location and identification for distribution network with special loads[J].Electric Power Automation Equipment,2020,40(8): |
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| 摘要: |
| 特殊负荷接入配电网造成原配电网潮流分布灵活多变，故障过电流方向不唯一，导致传统配电网故障定位与识别方法不具备自适应性，因此提出一种适用于含特殊负荷的配电网故障定位与识别方法。将配电网划分为多个双端无分支的区域网络进行分层降维，从区域端口的微型相量测量单元测量数据中挖掘故障电流信息，对基于正序电流故障分量的故障方向判据进行修正；然后在故障方向编码的基础上，将量子免疫优化算法用于实现配电网分层定位；最后在故障区域内，利用故障相与节点零序电流对故障类型进行快速有效判别。算例仿真结果表明，所提方法能够准确定位与识别含特殊负荷配电网故障区段，同时能有效避免非故障状态下的电力系统扰动对故障定位的影响。 |
| 关键词: 特殊负荷 配电网 故障定位 微型相量测量单元 量子免疫优化算法 |
| DOI：10.16081/j.epae.202008034 |
| 分类号:TM727.2 |
| 基金项目:国家自然科学基金资助项目(51767013)；甘肃省重点研发计划资助项目(18YF1FA058)；兰州市人才计划项目(2017-RC-95) |
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| Fault location and identification for distribution network with special loads |
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GAO Fengyang1,2, LI Zhaojun1, YUAN Cheng1, QI Xiaodong1, LI Xiaofeng1, ZHUANG Shengxian3, LI Haowu2
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1.School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China;2.Gansu Jiaoda Engineering Inspection Co.,Ltd.,Lanzhou 730070, China;3.School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
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| Abstract: |
| After the special loads are connected to the distribution network, the original distribution network’s power flow become flexible and changeable, and the direction of fault overcurrent is not unique, which makes the traditional fault location and identification methods failed for lacking self-adaptive, so a fault location and identification method for distribution network with special loads is proposed. The system is divided into several two-terminal sections to reduce dimension, the fault current information is mined from the data measured by micro PMU(Phasor Measurement Unit) of terminal sections, and the fault direction criterion based on the positive-sequence fault current is modified. Then based on the coding of the fault direction, the quantum immune optimization algorithm is used to locate fault section of the distribution network. Finally, in each fault section, the fault type can be identified quickly and effectively by using fault phase and the node zero-sequence current. Case simulative results show that the proposed method can accurately locate and identify the fault section of distribution network with special loads and effectively avoid the influence of power system disturbance on fault location under non-fault condition. |
| Key words: special load distribution network electric fault location micro PMU quantum immune optimization algorithm |
