Reliability estimation of high voltage SF6 circuit breakers by statistical analysis on the basis of the field data-Reference-Cited by-同舟云学术

Reliability estimation of high voltage SF6 circuit breakers by statistical analysis on the basis of the field data

Published:2013-10 Issue: Volume:103 Page:105-113
ISSN:0378-7796
Container-title:Electric Power Systems Research
language:en
Short-container-title:Electric Power Systems Research

Author:

Zhang Xiang,Gockenbach Ernst,Liu Zhaolin,Chen Haibo,Yang Linghui

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference25 articles.

1. Study of stresses of circuit-breakers: combined statistical method;Choonhapran,2006

2. The first international enquiry on circuit breaker failures & defects in service;Mazza;ELECTRA,1985

3. Summary of Cigré Working Group 13-06 studies on the test and control methods intended to assure the reliability of high voltage circuit breakers;Michaca;ELECTRA,1985

4. Interim report on the second international enquiry of the reliability of high voltage single pressure SF6 circuit breakers;Bargigia,1990

5. High voltage circuit breaker reliability data for use in system reliability studies–interim report;Bargigia,1991

Cited by 36 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Review of Digital Vibration Signal Analysis Techniques for Fault Diagnosis of High-Voltage Circuit Breakers;IEEE Transactions on Dielectrics and Electrical Insulation;2024-02

2. Fault Diagnosis of Medium Voltage Circuit Breakers Based on Vibration Signal Envelope Analysis;Sensors;2023-10-09

3. Light-induced thermoelastic sensor for ppb-level H2S detection in a SF6 gas matrices exploiting a mini-multi-pass cell and quartz tuning fork photodetector;Photoacoustics;2023-10

4. Light-Induced Thermoelastic Sensor for Ppb-Level H2s Detection In Sf6 Gas Matrices Exploiting a Mini-Multi-Pass Cell and Quartz Tuning Fork Photodetector;2023

5. Fault Diagnosis of High-Voltage Circuit Breakers via Hybrid Classifier by DS Evidence Fusion Algorithm;International Conference on Neural Computing for Advanced Applications;2023