EVERGREEN

Joint Journal of Novel Carbon Resource Sciences and Green Asia Strategy

ISSN:2189-0420 (Print until Mar 2020)
ISSN:2432-5953 (Online)

SCImago Journal & Country Rank

Open Access
Scopus
Google Scholar
Crossref
SCImago Journal & Country Rank
4.3
2024CiteScore
 
69th percentile
Powered by Scopus
Metrics by SCOPUS 2024
CiteScore
4.3
SJR
0.391
SNIP
1.192

Phasor Measurement Unit based Fault Detection using Tellegen's Theorem in Geographically Zoned Power Systems

Sunil Kumar1,*, Mohammad Anas1, Md. Fazle Rasool1, Ikbal Ali1
1Electrical Engineering, Jamia Millia Islamia, India
*Author to whom correspondence should be addressed:
E-mail: sunil.kumar.example@university.edu (SK)
Evergreen, Vol. 12, Iss. 02, pp. 1051–1062, 2025
DOI: 10.5109/7363494
Creative Commons Attribution 4.0 International Creative Commons Attribution 4.0 International
Received: November 28, 2024 | Revised: March 07, 2025 | Accepted: April 24, 2025 | Published: June 2025
Abstract
To ensure system stability and minimize downtime, it is imperative to rapidly and precisely identify faults in power systems. This study proposes a method that utilizes Phasor Measurement Units (PMUs) and Tellegen's theorem for fault detection. The power system is divided into zones based on geographical and electrical attributes, with PMUs strategically positioned in each zone to monitor voltage and current phasors. By applying Tellegen's theorem to the collected data, faults within these zones can be pinpointed. Experimental results on an 8-bus power system demonstrate the effectiveness of this approach, showing both accurate fault identification and reduced computational complexity. The simulation modeling of IEEE 8 bus system is performed on the platform real time digital simulator (RTDS/RSCAD).
Keywords
Fault detection ; Tellegen’s theorem ; RTDS ; open circuit and closed-circuit faults ; synchrophasor unit
Available Repositories
Journal DOI Zenodo Archive
Share Article
Article Metrics
--
Views
--
Downloads
--
Citations
Export Citation
BibTeX RIS Semantic Scholar CrossRef Mendeley Google Scholar
Full Text
Download PDF
References
  1. 1) J.Li, and S. Jiang, "Global energy interconnection: an effective solution to climate challenges," Global Energy Interconnection, 1 (4) 406-408 (2018) doi:10.1016/S2096-5117(18)30075-6
  2. 2) I.ALI, M.A. AFTAB, and S.M.S. HUSSAIN, "Performance comparison of iec 61850-90-5 and ieee c37.118.2 based wide area pmu communication networks," Journal of Modern Power Systems and Clean Energy, 4 (3) 487-495 (2016) doi:10.1007/s40565-016-0210-y
  3. 3) R.Isermann, "Model-based fault-detection and diagnosis – status and applications," Annu Rev Control, 29 (1) 71-85 (2005) doi:10.1016/j.arcontrol.2004.12.002
  4. 4) S.Beheshtaein, R. Cuzner, M. Savaghebi, and J.M. Guerrero, "Review on microgrids protection," IET Generation, Transmission & Distribution, 13 (6) 743-759 (2019) doi:10.1049/iet-gtd.2018.5212
  5. 5) B.J.Brearley, and R.R. Prabu, "A review on issues and approaches for microgrid protection," Renewable and Sustainable Energy Reviews, 67 988-997 (2017) doi:10.1016/j.rser.2016.09.047
  6. 6) L.Sun, and F. You, "Machine learning and data-driven techniques for the control of smart power generation systems: an uncertainty handling perspective," Engineering, 7 (9) 1239-1247 (2021) doi:10.1016/j.eng.2021.04.020
  7. 7) S.Kar, S.R. Samantaray, and M.D. Zadeh, "Data-mining model based intelligent differential microgrid protection scheme," IEEE Syst J, 11 (2) 1161-1169 (2017) doi:10.1109/JSYST.2014.2380432
  8. 8) M.Mansouri, M.-F. Harkat, H.N. Nounou, and M.N. Nounou, "Model-based approaches for fault detection," in: Data-Driven and Model-Based Methods for Fault Detection and Diagnosis, Elsevier, 2020: pp. 221-258 doi:10.1016/B978-0-12-819164-4.00015-7
  9. 9) M.Hojabri, U. Dersch, A. Papaemmanouil, and P. Bosshart, "A comprehensive survey on phasor measurement unit applications in distribution systems," Energies (Basel), 12 (23) 4552 (2019) doi:10.3390/en12234552
  10. 10) S.Samantaray, and N.K. Sharma, "PMU Assisted Integrated Impedance Angle-Based Microgrid Protection Scheme," in: 2022 IEEE Power & Energy Society General Meeting (PESGM), IEEE, 2022: pp. 1-1 doi:10.1109/PESGM48719.2022.9916954
  11. 11) Y.Bansal, and R. Sodhi, "PMUs enabled tellegen’s theorem-based fault identification method for unbalanced active distribution network using rtds," IEEE Syst J, 14 (3) 4567-4578 (2020) doi:10.1109/JSYST.2020.2976736
  12. 12) H.F.Habib, M.M. Esfahani, and O. Mohammed, "Development of Protection Scheme for Active Distribution Systems with Penetration of Distributed Generation," in: SoutheastCon 2018, IEEE, 2018: pp. 1-7 doi:10.1109/SECON.2018.8479115
  13. 13) A.Soleimanisardoo, H. Kazemi Karegar, and H.H. Zeineldin, "Differential frequency protection scheme based on off-nominal frequency injections for inverter-based islanded microgrids," IEEE Trans Smart Grid, 10 (2) 2107-2114 (2019) doi:10.1109/TSG.2017.2788851
  14. 14) S.Kumar, I. Ali, and A. Siddiqui, "Protection of microgrid feeders using impedance angle analysis assisted by synchrophasor units," Journal of Renewable Energy and Environment, 11 (3) 109-119 (2024) doi:10.30501/jree.2024.432832.1794
  15. 15) C.Wang, M. Wang, B. Yang, and K. Song, "A model-based method for bearing fault detection using motor current," J Phys Conf Ser, 1650 (3) 032130 (2020) doi:10.1088/1742-6596/1650/3/032130
  16. 16) X.G.Magagula, Y. Hamam, J.A. Jordaan, and A.A. Yusuff, "Fault detection and classification method using DWT and SVM in a power distribution network," in: 2017 IEEE PES PowerAfrica, IEEE, 2017: pp. 1-6 doi:10.1109/PowerAfrica.2017.7991190
  17. 17) T.Nagpal, and Y.S. Brar, "Expert system based fault detection of power transformer," J Comput Theor Nanosci, 12 (2) 208-214 (2015) doi:10.1166/jctn.2015.3719
  18. 18) X.Fan, and M.J. Zuo, "Fault diagnosis of machines based on d–s evidence theory. part 1: d–s evidence theory and its improvement," Pattern Recognit Lett, 27 (5) 366-376 (2006) doi:10.1016/j.patrec.2005.08.025
  19. 19) M.R.Zaidan, "Power System Fault Detection, Classification And Clearance By Artificial Neural Network Controller," in: 2019 Global Conference for Advancement in Technology (GCAT), IEEE, 2019: pp. 1-5 doi:10.1109/GCAT47503.2019.8978400
  20. 20) X.Wang, Z. Li, J. Liang, and Y. Li, "A Deep Double-Convolutional Neural Network-Based Fault Detection," in: 2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference (ONCON), IEEE, 2023: pp. 1-6 doi:10.1109/ONCON60463.2023.10431392
  21. 21) N.Ramesh Babu, and B. Jagan Mohan, "Fault classification in power systems using emd and svm," Ain Shams Engineering Journal, 8 (2) 103-111 (2017) doi:10.1016/j.asej.2015.08.005
  22. 22) H.A.Illias, X.R. Chai, and A.H. Abu Bakar, "Hybrid modified evolutionary particle swarm optimisation-time varying acceleration coefficient-artificial neural network for power transformer fault diagnosis," Measurement, 90 94-102 (2016) doi:10.1016/j.measurement.2016.04.052
  23. 23) M.Korkali, H. Lev-Ari, and A. Abur, "Traveling-wave-based fault-location technique for transmission grids via wide-area synchronized voltage measurements," IEEE Transactions on Power Systems, 27 (2) 1003-1011 (2012) doi:10.1109/TPWRS.2011.2176351
  24. 24) S.Saha, A. Bag, D. Basu Roy, S. Patranabis, and D. Mukhopadhyay, "Fault Template Attacks on Block Ciphers Exploiting Fault Propagation," in: 2020: pp. 612-643 doi:10.1007/978-3-030-45721-1_22
  25. 25) G.Chen, Q. Duan, C. Zhao, H. Wang, G. Sha, J. Gao, Y. Li, and S. Zhou, "Novel fault protection method for flexible dc power systems," Energies (Basel), 17 (14) 3446 (2024) doi:10.3390/en17143446
  26. 26) S.Pan, T. Morris, and U. Adhikari, "Classification of disturbances and cyber-attacks in power systems using heterogeneous time-synchronized data," IEEE Trans Industr Inform, 11 (3) 650-662 (2015) doi:10.1109/TII.2015.2420951
  27. 27) T.Yildiz, and A. Abur, "Convolutional neural network-assisted fault detection and location using few pmus," Electric Power Systems Research, 235 110705 (2024) doi:10.1016/j.epsr.2024.110705
  28. 28) I.Pavičić, N. Holjevac, I. Ivanković, and D. Brnobić, "Model for 400 kv transmission line power loss assessment using the pmu measurements," Energies (Basel), 14 (17) 5562 (2021) doi:10.3390/en14175562
  29. 29) Y.Shu, and W. Chen, "Research and application of uhv power transmission in china," High Voltage, 3 (1) 1-13 (2018) doi:10.1049/hve.2018.0003
  30. 30) M.B.K.Bouzid, and G. Champenois, "New expressions of symmetrical components of the induction motor under stator faults," IEEE Transactions on Industrial Electronics, 60 (9) 4093-4102 (2013) doi:10.1109/TIE.2012.2235392
  31. 31) M.Majidi, and M. Etezadi-Amoli, "A new fault location technique in smart distribution networks using synchronized/nonsynchronized measurements," IEEE Transactions on Power Delivery, 33 (3) 1358-1368 (2018) doi:10.1109/TPWRD.2017.2787131
  32. 32) M.Memarzadeh, B. Matthews, and I. Avrekh, "Unsupervised anomaly detection in flight data using convolutional variational auto-encoder," Aerospace, 7 (8) 115 (2020) doi:10.3390/aerospace7080115
  33. 33) J.-D.Park, and J. Candelaria, "Fault detection and isolation in low-voltage dc-bus microgrid system," IEEE Transactions on Power Delivery, 28 (2) 779-787 (2013) doi:10.1109/TPWRD.2013.2243478
  34. 34) Z.Huang, and Z. Wang, "A multiswitch open-circuit fault diagnosis of microgrid inverter based on slidable triangularization processing," IEEE Trans Power Electron, 36 (1) 922-930 (2021) doi:10.1109/TPEL.2020.3004531
  35. 35) S.A.Hosseini, H.A. Abyaneh, S.H.H. Sadeghi, F. Razavi, and A. Nasiri, "An overview of microgrid protection methods and the factors involved," Renewable and Sustainable Energy Reviews, 64 174-186 (2016) doi:10.1016/j.rser.2016.05.089
  36. 36) N.Sonule, and Prof.V. Madekar, "Short circuit fault detection and protection of dc microgrid," Int J Res Appl Sci Eng Technol, 11 (6) 4082-4087 (2023) doi:10.22214/ijraset.2023.54397
  37. 37) S.Das, S. Santoso, A. Gaikwad, and M. Patel, "Impedance-based fault location in transmission networks: theory and application," IEEE Access, 2 537-557 (2014) doi:10.1109/ACCESS.2014.2323353
  38. 38) S.H.Mortazavi, and J. Sadeh, "An analytical fault location method based on minimum number of installed pmus," International Transactions on Electrical Energy Systems, 26 (2) 253-273 (2016) doi:10.1002/etep.2075
  39. 39) S.Das, S.P. Singh, and B.K. Panigrahi, "Transmission line fault detection and location using wide area measurements," Electric Power Systems Research, 151 96-105 (2017) doi:10.1016/j.epsr.2017.05.025
  40. 40) Y.Bansal, and R. Sodhi, "PMUs enabled tellegen’s theorem-based fault identification method for unbalanced active distribution network using rtds," IEEE Syst J, 14 (3) 4567-4578 (2020) doi:10.1109/JSYST.2020.2976736
  41. 41) W.Li, D. Deka, M. Chertkov, and M. Wang, "Real-time faulted line localization and pmu placement in power systems through convolutional neural networks," IEEE Transactions on Power Systems, 34 (6) 4640-4651 (2019) doi:10.1109/TPWRS.2019.2917794
  42. 42) C.Allioua, A. Mingotti, R. Tinarelli, L. Peretto, and G. Frigo, "Cloud-Based PMUs for Real-Time Power System Monitoring: Theoretical and Experimental Analysis," in: 2023 IEEE 13th International Workshop on Applied Measurements for Power Systems (AMPS), IEEE, 2023: pp. 01-06 doi:10.1109/AMPS59207.2023.10297270
  43. 43) S.Ghosh, Y.J. Isbeih, S.K. Azman, M.S. El Moursi, and E. El-Saadany, "Optimal pmu allocation strategy for completely observable networks with enhanced transient stability characteristics," IEEE Transactions on Power Delivery, 37 (5) 4086-4102 (2022) doi:10.1109/TPWRD.2022.3144462
  44. 44) E.Casagrande, W.L. Woon, H.H. Zeineldin, and D. Svetinovic, "A differential sequence component protection scheme for microgrids with inverter-based distributed generators," IEEE Trans Smart Grid, 5 (1) 29-37 (2014) doi:10.1109/TSG.2013.2251017
Other Papers in This Issue