A Review and Taxonomy on Fault Analysis in Transmission Power Systems

被引:5
作者
Al Mtawa, Yaser [1 ]
Haque, Anwar [2 ]
Halabi, Talal [3 ]
机构
[1] Univ Winnipeg, Dept Appl Comp Sci, Winnipeg, MB R3B 2E9, Canada
[2] Western Univ, Dept Comp Sci, London, ON N6A 5B7, Canada
[3] Laval Univ, Dept Comp Sci & Software Engn, Quebec City, PQ G1V 0A6, Canada
关键词
power system; smart grid; fault analysis; fault identification; fault detection; fault localization; SUPPORT VECTOR MACHINE; KALMAN FILTER; LOCATION TECHNIQUE; GENETIC-ALGORITHM; BAYESIAN NETWORKS; NEURAL-NETWORK; SHORT-CIRCUIT; CLASSIFICATION; LINES; MODEL;
D O I
10.3390/computation10090144
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Enhancing resiliency in a power grid system is one of the core mandates of electrical distribution companies to provide high-level service. The power resiliency research community has proposed numerous schemes, to detect, classify, and localize fault events. However, the literature still lacks a comprehensive taxonomy of these schemes which can help advance future research. This study aims to provide a compact yet comprehensive review of the state-of-the-art solutions to fault analysis in transmission power systems. We discuss fault types and several fault-analysis methodologies adopted by relevant research works, propose a novel framework to classify these works, and highlight their strengths and limitations. We anticipate that this brief review would be helpful as a literature review and benefit the research community in choosing suitable techniques for fault analysis.
引用
收藏
页数:19
相关论文
共 104 条
[1]  
Abass A.Z., 2021, J ROBOT CONTROL, V2, P134
[2]   IoT-Enabled Smart Energy Grid: Applications and Challenges [J].
Abir, S. M. Abu Adnan ;
Anwar, Adnan ;
Choi, Jinho ;
Kayes, A. S. M. .
IEEE ACCESS, 2021, 9 :50961-50981
[3]   Hurricane Sandy: Lessons Learned, Again [J].
Abramson, David M. ;
Redlener, Irwin .
DISASTER MEDICINE AND PUBLIC HEALTH PREPAREDNESS, 2012, 6 (04) :328-329
[4]   A novel fault classification technique for double-circuit lines based on a combined unsupervised/supervised neural network [J].
Aggarwal, RK ;
Xuan, QY ;
Dunn, RW ;
Johns, AT ;
Bennett, A .
IEEE TRANSACTIONS ON POWER DELIVERY, 1999, 14 (04) :1250-1256
[5]   Reliability-based fault analysis models with industrial applications: A systematic literature review [J].
Ahmed, Qadeer ;
Raza, Syed Asif ;
Al-Anazi, Dahham M. .
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2021, 37 (04) :1307-1333
[6]  
Al Mtawa Y., 2021, P 2021 INT WIRELESS, P1569
[7]  
Al Mtawa Y, 2016, IEEE WCNC
[8]   Identifying Bounds on Sensing Coverage Holes in IoT Deployments [J].
Al Mtawa, Yaser ;
Hassanein, Hossam S. ;
Nasser, Nidal .
2015 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2015,
[9]   Decision tree analysis to identify harmful contingencies and estimate blackout indices for predicting system vulnerability [J].
Aliyan, Ehsan ;
Aghamohammadi, Mohammadreza ;
Kia, Mohsen ;
Heidari, Alireza ;
Shafie-khah, Miadreza ;
Catalao, Joao P. S. .
ELECTRIC POWER SYSTEMS RESEARCH, 2020, 178
[10]   Locating Faults on Untransposed, Meshed Transmission Networks Using a Limited Number of Synchrophasor Measurements [J].
Azizi, Sadegh ;
Sanaye-Pasand, Majid ;
Paolone, Mario .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2016, 31 (06) :4462-4472