海底观测网光电复合缆开路故障识别及区间定位方法

FENG Yingbin

您当前的位置：

首页 >

文章列表页 >

海底观测网光电复合缆开路故障识别及区间定位方法

更新时间：2025-08-13

- 海底观测网光电复合缆开路故障识别及区间定位方法
- Automation of Electric Power Systems Vol. 39, Issue 10, Pages: 151-156(2015)
- 作者机构：
  
  机器人学国家重点实验室, 中国科学院沈阳自动化研究所,辽宁省,沈阳市,110016
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2015
- 稿件说明：
移动端阅览
FENG Yingbin. [J]. Automation of electric power systems, 2015, 39(10): 151-156.
DOI：

FENG Yingbin. [J]. Automation of electric power systems, 2015, 39(10): 151-156. DOI：

摘要

针对海底观测网络海底光电复合缆出现开路故障的问题

提出了采用平均测量残差值的方法识别开路故障。根据实时的网络拓扑结构与电流方向

建立有向图的邻接矩阵

将网络结构分成三种不同的区域。计算故障时刻前后的接驳盒电压变化差

利用差值进行开路故障的区域定位

针对耦合区域

提出了假设检验的方法进行开路故障海缆的区间定位。建立了海底观测网供电网络模型

进行了开路故障仿真分析

结果验证了该开路故障识别和定位方法的实用性和有效性。

Abstract

For the problem of open-circuit fault with the optoelectric cable in the seafloor observatory network

a method of its identification by measuring the mean residual value is proposed. According to real-time network topology and current direction

an adjacency matrix of directed graph is built

to divide the network structure into three different areas. The voltage difference of the junction box across the moment the fault occurs is calculated and used for open-circuit fault section location. A hypothesis-testing method for the coupled regions is proposed to locate the section of the open-circuit fault. A model for the seafloor observatory network is developed and the open-circuit fault is analyzed by simulation. The results show that the method proposed is practical and efficient.

关键词

Keywords

references

Views

2453

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Distribution Network Restoration Decision Considering Risk of State Estimation Error in Restoration Process

Time-varying Virtual Inertia and State Estimation of Grid-following Converter Based on Maximum Correntropy Criterion and Polynomial Chaos Kalman Filter

Forecasting-aided State Estimation for Distribution Networks Based on Event-triggering Encryption

Related Author

ZHU Kaiyu

LIN Jikeng

HAO Lili

KAN Yizhou

XUE Yusheng

XUE Feng

WANG Yuhong

WANG Xueke

Related Institution

College of Electronics and Information Engineering, Tongji University

College of Electrical Engineering & Control Science, Nanjing Tech University

State Grid Electric Power Research Institute (NARI Group Corporation)

School of Electrical Engineering, Sichuan University

State Grid Urumqi Power Supply Company

⁰