Loading [Contrib]/a11y/accessibility-menu.js
Research article

Adaptive OCR coordination in distribution system with distributed energy resources contribution

  • Received: 05 October 2023 Revised: 21 November 2023 Accepted: 22 November 2023 Published: 30 November 2023
  • More and more distributed energy resources (DERs) are being added to the medium-voltage (MV) or low-voltage (LV) radial distribution networks (RDNs). These distributed power sources will cause the redistribution of power flow and fault current, bringing new challenges to the coordination of power system protection. An adaptive protection coordination strategy is proposed in this paper. It will trace the connectivity of the system structure to determine the set of relay numbers as a tracking path. According to the topology of the system structure, the tracking path can be divided into two categories: the main feeder path and the branch path. The time multiplier setting (TMS) of each relay can be used to evaluate the operation time of the over-current relay (OCR), and the operation time of the relay can be used to evaluate the fitness of the TMS setting combination. Furthermore, the relay protection coordination problem can be modeled to minimize the accumulated summation of all primary and backup relay operation time (OT) subject to the coordination time interval (CTI) limitation. A modified particle swarm optimization (MPSO) algorithm with adaptive self-cognition and society operation scheme (ASSOS) was proposed and utilized to determine TMS for each relay on the tracking path. A 16-bus test MV system with distributed generators (DGs) will be applied to test the adaptive protection coordination approach proposed in this paper. The results show that the proposed MPSO algorithm reduces the overall OT and relieves the impact on protection coordination settings after DG joins the system. The paper also tests and compares the proposed MPSO with other metaheuristic intelligence-based random search algorithms to prove that MPSO possesses with increased efficiency and performance.

    Citation: Tung-Sheng Zhan, Chun-Lien Su, Yih-Der Lee, Jheng-Lun Jiang, Jin-Ting Yu. Adaptive OCR coordinationin distribution system with distributed energy resources contribution[J]. AIMS Energy, 2023, 11(6): 1278-1305. doi: 10.3934/energy.2023058

    Related Papers:

    [1] Eunha Shim, Beth Kochin, Alison Galvani . Insights from epidemiological game theory into gender-specific vaccination against rubella. Mathematical Biosciences and Engineering, 2009, 6(4): 839-854. doi: 10.3934/mbe.2009.6.839
    [2] Hyun Mo Yang, André Ricardo Ribas Freitas . Biological view of vaccination described by mathematical modellings: from rubella to dengue vaccines. Mathematical Biosciences and Engineering, 2019, 16(4): 3195-3214. doi: 10.3934/mbe.2019159
    [3] Lili Liu, Xi Wang, Yazhi Li . Mathematical analysis and optimal control of an epidemic model with vaccination and different infectivity. Mathematical Biosciences and Engineering, 2023, 20(12): 20914-20938. doi: 10.3934/mbe.2023925
    [4] Pannathon Kreabkhontho, Watchara Teparos, Thitiya Theparod . Potential for eliminating COVID-19 in Thailand through third-dose vaccination: A modeling approach. Mathematical Biosciences and Engineering, 2024, 21(8): 6807-6828. doi: 10.3934/mbe.2024298
    [5] Eunha Shim . Optimal strategies of social distancing and vaccination against seasonal influenza. Mathematical Biosciences and Engineering, 2013, 10(5&6): 1615-1634. doi: 10.3934/mbe.2013.10.1615
    [6] Majid Jaberi-Douraki, Seyed M. Moghadas . Optimal control of vaccination dynamics during an influenza epidemic. Mathematical Biosciences and Engineering, 2014, 11(5): 1045-1063. doi: 10.3934/mbe.2014.11.1045
    [7] Xunyang Wang, Canyun Huang, Yuanjie Liu . A vertically transmitted epidemic model with two state-dependent pulse controls. Mathematical Biosciences and Engineering, 2022, 19(12): 13967-13987. doi: 10.3934/mbe.2022651
    [8] Hamed Karami, Pejman Sanaei, Alexandra Smirnova . Balancing mitigation strategies for viral outbreaks. Mathematical Biosciences and Engineering, 2024, 21(12): 7650-7687. doi: 10.3934/mbe.2024337
    [9] Lan Zou, Jing Chen, Shigui Ruan . Modeling and analyzing the transmission dynamics of visceral leishmaniasis. Mathematical Biosciences and Engineering, 2017, 14(5&6): 1585-1604. doi: 10.3934/mbe.2017082
    [10] Hai-Feng Huo, Tian Fu, Hong Xiang . Dynamics and optimal control of a Zika model with sexual and vertical transmissions. Mathematical Biosciences and Engineering, 2023, 20(5): 8279-8304. doi: 10.3934/mbe.2023361
  • More and more distributed energy resources (DERs) are being added to the medium-voltage (MV) or low-voltage (LV) radial distribution networks (RDNs). These distributed power sources will cause the redistribution of power flow and fault current, bringing new challenges to the coordination of power system protection. An adaptive protection coordination strategy is proposed in this paper. It will trace the connectivity of the system structure to determine the set of relay numbers as a tracking path. According to the topology of the system structure, the tracking path can be divided into two categories: the main feeder path and the branch path. The time multiplier setting (TMS) of each relay can be used to evaluate the operation time of the over-current relay (OCR), and the operation time of the relay can be used to evaluate the fitness of the TMS setting combination. Furthermore, the relay protection coordination problem can be modeled to minimize the accumulated summation of all primary and backup relay operation time (OT) subject to the coordination time interval (CTI) limitation. A modified particle swarm optimization (MPSO) algorithm with adaptive self-cognition and society operation scheme (ASSOS) was proposed and utilized to determine TMS for each relay on the tracking path. A 16-bus test MV system with distributed generators (DGs) will be applied to test the adaptive protection coordination approach proposed in this paper. The results show that the proposed MPSO algorithm reduces the overall OT and relieves the impact on protection coordination settings after DG joins the system. The paper also tests and compares the proposed MPSO with other metaheuristic intelligence-based random search algorithms to prove that MPSO possesses with increased efficiency and performance.





    [1] Holguin JP, Rodriguez DC, Ramos G (2020) Reverse power flow (RPF) detection and impact on protection coordination of distribution systems. IEEE Trans Ind Appl 56: 2393–2401. https://doi.org/10.1109/TIA.2020.2969640 doi: 10.1109/TIA.2020.2969640
    [2] Zeineldin HH, Mohamed YARI, Khadkikar V, et al. (2013) A protection coordination index for evaluating distributed generation impacts on protection for meshed distribution systems. IEEE Trans Smart Grid 4: 1523–1532. https://doi.org/10.1109/TSG.2013.2263745 doi: 10.1109/TSG.2013.2263745
    [3] Wan H, Li KK, Wong KP (2010) An adaptive multiagent approach to protection relay coordination with distributed generators in industrial power distribution system. IEEE Trans Ind Appl 46: 2118–2124. https://doi.org/10.1109/TIA.2010.2059492 doi: 10.1109/TIA.2010.2059492
    [4] Isherwood N, Rahman MS, Oo AMT (2017) Distribution feeder protection and reconfiguration using multi-agent approach. Proceeding of Australasian Universities Power Engineering Conference (AUPEC) 1–6. https://doi.org/10.1109/AUPEC.2017.8282425 doi: 10.1109/AUPEC.2017.8282425
    [5] Kayyali D, Zeineldin H, Diabat A, et al. (2020) An optimal integrated approach considering distribution system reconfiguration and protection coordination. Proceeding of 2020 IEEE Power & Energy Society General Meeting (PESGM) 1–5. https://doi.org/10.1109/PESGM41954.2020.9281412 doi: 10.1109/PESGM41954.2020.9281412
    [6] Ghotbi-Maleki M, Chabanloo RM, Zeineldin HH, et al. (2021) Design of setting group-based overcurrent protection scheme for active distribution networks using MILP. IEEE Trans Smart Grid 12: 1185–1193. https://doi.org/10.1109/TSG.2020.3027371 doi: 10.1109/TSG.2020.3027371
    [7] Alam MN, Chakrabarti S, Tiwari VK (2020) Protection coordination with high penetration of solar power to distribution networks. Proceeding of 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES) 132–137. https://doi.org/10.1109/SPIES48661.2020.9243146 doi: 10.1109/SPIES48661.2020.9243146
    [8] Abdul Rahim MN, Mokhlis H, Bakar AHA, et al. (2019) Protection coordination toward optimal network reconfiguration and DG Sizing. IEEE Access 7: 163700–163718. https://doi.org/10.1109/ACCESS.2019.2952652 doi: 10.1109/ACCESS.2019.2952652
    [9] Zhan H, Wang C, Wang Y, et al. (2016) Relay protection coordination integrated optimal placement and sizing of distributed generation sources in distribution networks. IEEE Trans Smart Grid 7: 55–65. https://doi.org//10.1109/TSG.2015.2420667 doi: 10.1109/TSG.2015.2420667
    [10] Pedraza A, Reyes D, Gomez C, et al. (2015) Optimization methodology to distributed generation location in distribution networks assessing protections coordination. IEEE Latin America Trans 13: 1398–1406. https://doi.org//10.1109/TLA.2015.7111995 doi: 10.1109/TLA.2015.7111995
    [11] Saldarriaga-Zuluaga SD, López-Lezama JM, Muñoz-Galeano N (2021) Adaptive protection coordination scheme in microgrids using directional over-current relays with non-standard characteristics. Heliyon 7: e06665. https://doi.org/10.1016/j.heliyon.2021.e06665 doi: 10.1016/j.heliyon.2021.e06665
    [12] Mahat P, Chen Z, Bak-Jensen B, et al. (2011) A simple adaptive overcurrent protection of distribution systems with distributed generation. IEEE Trans Smart Grid 2: 428–437. https://doi.org//10.1109/TSG.2011.2149550 doi: 10.1109/TSG.2011.2149550
    [13] Jongepier AG, Van der Sluis L (1997) Adaptive distance protection of double-circuit lines using artificial neural networks. IEEE Trans on Power Delivery 12: 97–105. https://doi.org//10.1109/61.568229 doi: 10.1109/61.568229
    [14] Musirikare A, Pujiantara M, Tjahjono A, et al. (2018) ANN-based modeling of directional overcurrent relay characteristics applied in radial distribution system with distributed generations. Proceeding of 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE) 52–57. https://doi.org//10.1109/ICITEED.2018.8534834 doi: 10.1109/ICITEED.2018.8534834
    [15] Rahmatullah D, Dewantara BY, Iradiratu DPK (2018) Adaptive DOCR coordination in loop electrical distribution system with DG using artificial neural network LMBP. Proceeding of 2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) 560–565. https://doi.org//10.1109/ISRITI.2018.8864433 doi: 10.1109/ISRITI.2018.8864433
    [16] Chiang MY, Huang SC, Hsiao TC, et al. (2022) Optimal sizing and location of photovoltaic generation and energy storage systems in an unbalanced distribution system. Energies 15: 6682. https://doi.org/10.3390/en15186682 doi: 10.3390/en15186682
    [17] Javadian SAM, Tamizkar R, Haghifam MR (2009) A Protection and reconfiguration scheme for distribution networks with DG. Proceeding of 2009 IEEE Bucharest Power Tech Conference 1–8. https://doi.org/10.1109/PTC.2009.5282063 doi: 10.1109/PTC.2009.5282063
    [18] Akmal M, Al-Naemi F, Iqbal N, et al. (2019) Impact of distributed PV generation on relay coordination and power quality. Proceeding of 2019 IEEE Milan PowerTech 1–6. https://doi.org/10.1109/PTC.2019.8810791 doi: 10.1109/PTC.2019.8810791
    [19] Soni AK, Kumar A, Panda RK, et al. (2023) Adaptive coordination of relays in AC microgrid considering operational and topological changes. IEEE Systems Journal 17: 3071–3082. https://doi.org/10.1109/JSYST.2022.3227311 doi: 10.1109/JSYST.2022.3227311
    [20] The Institute of Electrical and Electronics Engineers, Inc. (2001) IEEE recommended practice for protection and coordination of industrial and commercial power systems, New York: IEEE press, 1–710.
    [21] Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks (ICNN'95) 4: 1942–1948. https://doi.org//10.1109/ICNN.1995.488968 doi: 10.1109/ICNN.1995.488968
    [22] Eberhart R, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the 2000 Congress on Evolutionary Computation (CEC00) 1: 84–88. https://doi.org//10.1109/CEC.2000.870279 doi: 10.1109/CEC.2000.870279
  • This article has been cited by:

    1. Impact of vaccine arrival on the optimal control of a newly emerging infectious disease: A theoretical study, 2012, 9, 1551-0018, 539, 10.3934/mbe.2012.9.539
    2. Bruno Buonomo, Modeling ITNs Usage: Optimal Promotion Programs Versus Pure Voluntary Adoptions, 2015, 3, 2227-7390, 1241, 10.3390/math3041241
    3. Hamadjam Abboubakar, Jean Claude Kamgang, Leontine Nkague Nkamba, Daniel Tieudjo, Bifurcation thresholds and optimal control in transmission dynamics of arboviral diseases, 2018, 76, 0303-6812, 379, 10.1007/s00285-017-1146-1
    4. Benjamin Riche, Hélène Bricout, Marie-Laure Kürzinger, Sylvain Roche, Jean Iwaz, Jean-François Etard, René Ecochard, Modeling and predicting the long-term effects of various strategies and objectives of varicella-zoster vaccination campaigns, 2016, 15, 1476-0584, 927, 10.1080/14760584.2016.1183483
    5. Lingcai Kong, Jinfeng Wang, Weiguo Han, Zhidong Cao, Modeling Heterogeneity in Direct Infectious Disease Transmission in a Compartmental Model, 2016, 13, 1660-4601, 253, 10.3390/ijerph13030253
    6. Nkengafac Villyen Motaze, Zinhle E. Mthombothi, Olatunji Adetokunboh, C. Marijn Hazelbag, Enrique M. Saldarriaga, Lawrence Mbuagbaw, Charles Shey Wiysonge, The Impact of Rubella Vaccine Introduction on Rubella Infection and Congenital Rubella Syndrome: A Systematic Review of Mathematical Modelling Studies, 2021, 9, 2076-393X, 84, 10.3390/vaccines9020084
    7. BRUNO BUONOMO, ON THE OPTIMAL VACCINATION STRATEGIES FOR HORIZONTALLY AND VERTICALLY TRANSMITTED INFECTIOUS DISEASES, 2011, 19, 0218-3390, 263, 10.1142/S0218339011003853
    8. Bruno Buonomo, 2014, Chapter 3, 978-3-319-06922-7, 23, 10.1007/978-3-319-06923-4_3
    9. Chairat Modnak, Jin Wang, Zindoga Mukandavire, Simulating optimal vaccination times during cholera outbreaks, 2014, 07, 1793-5245, 1450014, 10.1142/S1793524514500144
    10. Drew Posny, Jin Wang, Zindoga Mukandavire, Chairat Modnak, Analyzing transmission dynamics of cholera with public health interventions, 2015, 264, 00255564, 38, 10.1016/j.mbs.2015.03.006
    11. Matt J. Keeling, Andrew Shattock, Optimal but unequitable prophylactic distribution of vaccine, 2012, 4, 17554365, 78, 10.1016/j.epidem.2012.03.001
    12. Bruno Buonomo, Cruz Vargas-De-León, Effects of Mosquitoes Host Choice on Optimal Intervention Strategies for Malaria Control, 2014, 132, 0167-8019, 127, 10.1007/s10440-014-9894-z
    13. Bruno Buonomo, Piero Manfredi, Alberto d’Onofrio, Optimal time-profiles of public health intervention to shape voluntary vaccination for childhood diseases, 2019, 78, 0303-6812, 1089, 10.1007/s00285-018-1303-1
    14. Adison Thongtha, Chairat Modnak, Optimal COVID-19 epidemic strategy with vaccination control and infection prevention measures in Thailand, 2022, 7, 24680427, 835, 10.1016/j.idm.2022.11.002
    15. Calvin Tadmon, Arnaud Feukouo Fossi, Berge Tsanou, A two–strain avian–human influenza model with environmental transmission: Stability analysis and optimal control strategies, 2024, 10075704, 107981, 10.1016/j.cnsns.2024.107981
    16. Gui Guan, Zhenyuan Guo, Yanyu Xiao, Dynamical behaviors of a network-based SIR epidemic model with saturated incidence and pulse vaccination, 2024, 137, 10075704, 108097, 10.1016/j.cnsns.2024.108097
    17. Samiullah Salim, Fazal Dayan, Muhammad Azizur Rehman, Husam A. Neamah, Optimization and Control in Rubella Transmission Dynamics: A Boundedness-Preserving Numerical Model with Vaccination, 2024, 23529148, 101595, 10.1016/j.imu.2024.101595
    18. Habtamu Ayalew Engida, Demeke Fisseha, Malaria and leptospirosis co-infection: A mathematical model analysis with optimal control and cost-effectiveness analysis, 2025, 24682276, e02517, 10.1016/j.sciaf.2024.e02517
    19. Giovanni Ziarelli, Stefano Pagani, Nicola Parolini, Francesco Regazzoni, Marco Verani, A model learning framework for inferring the dynamics of transmission rate depending on exogenous variables for epidemic forecasts, 2025, 437, 00457825, 117796, 10.1016/j.cma.2025.117796
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1639) PDF downloads(53) Cited by(2)

Article outline

Figures and Tables

Figures(20)  /  Tables(9)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog