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Decentralized fault location, isolation and self restoration (FLISR) logic implementation using IEC 61850 GOOSE signals

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Fault location, isolation and self-restoration (FLISR) automation is an essential component of smart grids concept. It consists of a high level of comprehensive automation and monitoring of the distribution grid improving the quality of energy supplied to customers. This paper presents an algorithm for decentralized FLISR architecture with peer-to-peer communication using IEC 61860 GOOSE messages. An analysis of short circuit detection was presented due to the method of the grid earthing system. The proposed automation model was built based on communication logic between configured intelligent electronic devices (IED) from ABB and Siemens. The laboratory tests were conducted in a half-loop grid model with a bilateral power supply (typical urban grid). The laboratory research concerned three locations of short circuits: between substation and section point, between two section points and between section point and normally open point (NOP). The logic implementation was developed using State Sequencer software offered by Test Universe.
Rocznik
Strony
art. no. e143101
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
  • ENCO Sp. z o.o., Postępu 13, 02-676 Warsaw, Poland
  • Electrical Power Engineering Institute, Faculty of Electrical Engineering, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland
  • Electrical Power Engineering Institute, Faculty of Electrical Engineering, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland
  • Electrical Power Engineering Institute, Faculty of Electrical Engineering, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland
  • Electrical Power Engineering Institute, Faculty of Electrical Engineering, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland
Bibliografia
  • [1] G. Srinivasan, K. Amaresh, and K.R. Cheepathi, “Economic based evaluation of DGs in capacitor allocated optimal distribution network,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e139053, 2022, doi: 10.24425/bpasts.2022.139053.
  • [2] J. Quiros-Tortos and V. Terzija, “A Graph Theory Based New Approach for Power System Restoration,” in Proc. 2013 IEEE Grenoble PowerTech (POWERTECH), 2013, doi: 10.1109/ptc.2013.6652108.
  • [3] R.J. Agüero, “Applying Self-Healing Schemes to Modern Power Distribution Systems,” in Proc. 2012 IEEE Power and Energy Society General Meeting, 2012, doi: 10.1109/PESGM.2012.6344960.
  • [4] T.D. Sudhakar and K.N. Srinivs, “Power System Reconfiguration Based on Prim’s Algorithm,” in Proc. 2011 1st International Conference on Electrical Energy Systems (ICEES), 2011, doi: 10.1109/ICEES.2011.5725295.
  • [5] M.M. Ibrahim, H.A. Mostafa, M.M.A. Salama, R. El-Shatshat, and K.B. Shaban, “A Graph-theoretic Service Restoration Algorithm for Power Dystribution Systems,” in Proc. 2018 International Conference on Innovative Trends in Computer Engineering (ITCE), 2018, doi: 10.1109/itce.2018.8316647.
  • [6] L.A. Felber, F.P. Ribeiro, B.D. Bonatto, A.C.Z. De Souza, and S.A.J. Neto, “Low Cost Self-Healing Applied to Distribution Grid Supplying Brazilian Municipalities,” in Proc. 2015 IEEE PES Innovative Smart Grid Technologies Latin America (ISGT LATAM), 2015, doi: 10.1109/isgt-la.2015.7381170.
  • [7] J. Quiros-Tortos, M. Panteli, P. Wall, and V. Tereija, “Sectionalising methodology for paraller system restoration based on graph theory,” IET Gener. Transmiss. Distrib., vol. 9, no. 11, pp. 1216–1225, 2015, doi: 10.1049/iet-gtd.2014.0727.
  • [8] V. Balaji and S. Chitra, “Power quality management in electrical grid using SCANN controller-based UPQC,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e140257, 2022, doi: 10.24425/bpasts.2022.140257.
  • [9] S. Sannigrahi, S.R. Ghatak, and P. Acharjee, “Multi-objective optimisation-based active distribution system planning with reconfiguration, intermittent RES and DSTATCOM,” IET Renew. Power Gener., vol. 13, no. 13, pp. 2418–2429, 2019, doi: 10.1049/iet-rpg.2018.6060.
  • [10] A. Bonfilio, M. Invernizzi, A. Labella, and R. Procopio, “Design and Implementation of a Variable Synthetic Inertia Controller forWind Turbine Generators,” IEEE Trans. Power Syst., vol. 34, no. 1, pp. 754–764, 2019, doi: 10.1109/tpwrs.2018.2865958.
  • [11] P. Jamborsalmati, A. Sadu, F. Ponci, and A. Monti, “Implementation of an Agent based Distributed FLISR Algorithm using IEC 61850 in Active Distribution Grids,” in Proc. 2015 International Conference on Renewable Energy Research and Applications (ICRERA), 2015, doi: 10.1109/icrera.2015.7418485.
  • [12] R. Guo et al., “Fault Location, Isolation and Service Restoration – Optimizing Field Operations for Utilities,” in Proc. 2016 IEEE Rural Electric Power Conference (REPC), 2016, doi: 10.1109/repc.2016.14.
  • [13] P. Parikh, I. Voloh, and M. Mahony, “Isolation and Service Restoration (FLISR) Technique using IEC 61850 GOOSE,” in Proc. 2013 IEEE Power and Energy Society General Meeting, 2013, doi: 10.1109/pesmg.2013.6672862.
  • [14] T.H. Cormen, C.E. Leiserson, R.L. Rivest, and C. Stein, “Section 23.2: The algorithms of Kruscal and Prim,” in Introduction to Algorithms, 3rd ed., MIT Press, 2009, pp. 631–638.
  • [15] J. Ansari, A. Gholami, and A. Kazemi, “Multi-agent systems for reactive power control in smart grids,” Int. J. Electr. Power Energy Syst., vol. 83, pp. 411–425, 2016, doi: 10.1016/j.ijepes.2016.04.010.
  • [16] P.D. Duarte et al., “Substation-based Self-healing System with Advanced Features for Control and Monitoring of Distribution Systems,” in Proc. 2016 17th International Conference Harmonics and Quality of Power (ICHQP), 2016, doi: 10.1109/ichqp.2016.7783340.
  • [17] M. Eriksson, M. Armendariz, O. Vasilenko, A. Saleem, and L. Nordström, “Multi-Agent Based Distribution Automation Solution for Self-Healing Grids,” IEEE Trans. Ind. Electron., vol. 62, no. 4, pp. 2620–2628, 2015, doi: 10.1109/tie.2014.2387098.
  • [18] W.R. Uluski, “Using Distribution Automation for a Self-Healing Grid,” in Proc. PES T&D 2012, 2012, doi: 10.1109/tdc.2012.6281582.
  • [19] B.S. Torres, L.R. Ferreira, and A.R. Aoki, “Distributed Intelligent System for Self-Healing in Smart Grids,” IEEE Trans. Power Del., vol. 33, no. 5, pp. 2394–2403, 2018, doi: 10.1109/tpwrd.2018.2845695.
  • [20] X. Yang, Y. Zhang, H. He, S. Ren, and G. Weng, “Real-Time Demand Side Management for a Microgrid Considering Uncertainties,” IEEE Trans. Smart Grid, vol. 10, no. 3, pp. 3401–3414, 2019, doi: 10.1109/tsg.2018.2825388.
  • [21] A. Younesi, H. Shayeghi, A. Safari, and P. Siano, “Assessing the resilience of multi microgrid based widespread power systems against natural disasters using Monte Carlo Simulation,” Energy, vol. 207, p. 118220, 2020, doi: 10.1016/j.energy.2020.118220.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2ae76e44-9a73-441c-9873-7308d16d091c
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