The DOI matches its Crossref record, with a small difference in title or year; 1 of the 1 references it cites were found in a registry or an index.
A Comparative Review of Fault Location/Identification Methods in Distribution Networks
Abstract
Power failures are more frequent in Distribution Networks (DNs) compared to other sections of power system (generation and transmission). These failures are happened due to the occurrence of fault in the connected equipment or network. Consequently, the connected loads at consumer ends are affected until the service is restored. The time taken to restore the power or service back to the consumer may vary with the utilities based on the network complexity, design, availability of Fault Indicators (FI), etc. To improve the quality of restoring the service to the consumers, a faster service restoration technique needs to be adopted. The main challenge for the restoration process is the fault identification. Once the fault is identified, the service can be restored to the consumer within short time after proper isolation of the faulty section from the healthy portion of the network. In this paper, a comparative review of fault location/identification methods in DNs is carried out along with future research scopes in this area.
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“80% of faults occur in distribution lines; hence, this area is of particular importance for researchers [3].”
From Fault Detection and Fault Diagnosis in Power System Using AI: A Review (2024) · cites this paper as [14]
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Cites
Impedance-based fault location experience
Zimmerman, K., & Costello, D. (2005) · 58th Annual Conference for Protective Relay Engineers, 2005. · doi:10.1109/cpre.2005.1430435 · Cited by 104
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Research works printed but not linked: 40
- 10.1109/ICMA.2017.8015828 · DOI
- bahmanyar, 2016, An improved faultlocation method for distribution networks exploiting emerging LV smart meters, IEEE Workshop on Environmental Energy and Structural Monitoring Systems (EESMS), 1
- 10.1016/j.epsr.2014.02.002 · DOI
- 10.1109/TPWRD.2008.917919 · DOI
- mora-florez, 2007, Fault location in power distribution systems using a learning algorithm for multivariable data analysis, IEEE Trans Power Deliv, 22, 1715, 10.1109/TPWRD.2006.883021 · DOI
- javadian, 2009, Determining fault’s type and accurate location in distribution systems with DG using MLP neural networks, International Conference on Clean Electrical Power, 284
- 10.1109/TPWRD.2014.2372045 · DOI
- trindade, 2016, Low voltage zones to support fault location in distribution systems with smart meters, IEEE Trans Smart Grid, 1
- 10.1049/iet-gtd.2013.0633 · DOI
- 10.1109/PESS.1999.784386 · DOI
- saha, 2001, fault location method for mv cable network, Proceedings of 7th International Conference on Developments in Power Systems Protection (DPSP 2001), 323, 10.1049/cp:20010165 · DOI
- 10.1109/APPEEC.2017.8308948 · DOI
- 10.1109/REPCON.2007.369552 · DOI
- 10.1109/TPWRD.2010.2057454 · DOI
- 10.1109/TPWRD.2008.2002977 · DOI
- 10.1109/PESGM.2012.6345512 · DOI
- 10.1109/PESGM.2014.6939547 · DOI
- 10.1109/TPWRD.2014.2302137 · DOI
- 10.1016/j.ijepes.2006.03.001 · DOI
- borghetti, 2008, Continuous-wavelet transform for fault location in distribution power networks: definition of mother wavelets inferred from fault originated transients, IEEE Trans Power Syst, 23, 380, 10.1109/TPWRS.2008.919249 · DOI
- 10.1109/TPWRD.2010.2046655 · DOI
- thukaram, 2005, Artificial neural network and support vector machine approach for locating faults in radial distribution systems, IEEE Trans Power Deliv, 20, 710, 10.1109/TPWRD.2005.844307 · DOI
- 10.1109/PES.2010.5588189 · DOI
- 10.1016/j.ijepes.2014.12.014 · DOI
- el-fouly, 2009, on the compatibility of fault location approaches and distributed generation, 2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources Into the Power Delivery System CIGRE/PES, 1
- 10.1109/TDCLA.2006.311428 · DOI
- ramar, 0, Generalized Impedance-Based Fault Location for Distribution Systems, IEEE Trans Power Deliv, 27, 449
- 10.1109/CPRE.2015.7102173 · DOI
- 10.1109/TAPENERGY.2015.7229659 · DOI
- 10.1016/j.epsr.2007.05.010 · DOI
- 10.1109/28.259729 · DOI
- 10.1016/j.rser.2017.03.021 · DOI
- javadian, 2009, Determining fault’s type and accurate location in distribution systems with DG using MLP neural networks, International Conference on Clean Electrical Power, 284
- 10.1016/j.ijepes.2006.11.009 · DOI
- thukaram, 2005, Artificial neural network and support vector machine approach for locating faults in radial distribution systems, IEEE Trans Power Deliv, 20, 710, 10.1109/TPWRD.2005.844307 · DOI
- 10.1109/TPWRD.2010.2050218 · DOI
- 10.1109/ICHQP.2016.7783424 · DOI
- 10.1016/j.ijepes.2013.09.011 · DOI
- 10.1109/TPWRD.2014.2302137 · DOI
- 10.1109/TDCLA.2006.311428 · DOI
Cited by
Sorted by citations
Fault Detection and Fault Diagnosis in Power System Using AI: A Review
Nasim, S. F., Aziz, S., Qaiser, A., Kulsoom, U., & Ahmed, S. (2024) · Sir Syed University Research Journal of Engineering & Technology · doi:10.33317/ssurj.598 · Cited by 17
Metadata
- Authors
- Haleem, A. M. I., Sharma, M., Sajan, K. S., & Babu, K. N. D.
- Affiliations
- University of Petroleum and Energy Studies (IN)
- GE Energy Connections, Hyderabad, India
- Megger, Chennai, India
- ORCID
- Sharma, Madhu
- Venue
- 2018 1st International Conference on Advanced Research in Engineering Sciences (ARES)
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 1-6
- OpenAlex
- W2947857642
- Cited by
- 14
- References
- 1
- Citations by year
- 2025: 3 · 2024: 6 · 2023: 2 · 2022: 2 · 2021: 1
- FWCI
- 0.38
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Advanced Research in Engineering Sciences (ARES) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Power Systems Fault Detection
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical Fault Detection and Protection
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
- Keywords
- fault location, fault identification, distribution networks, service restoration, fault indicators, power outages, fault isolation, power quality
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