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Effective Power Restoration in the National Grid, Using Interconnected System (Neural Network Intelligence): A Review of the Nigerian Grid System

Received: 15 March 2023     Accepted: 14 June 2023     Published: 27 June 2023
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Abstract

Power demand is increasing with recent developments in technology to improve and facilitate the smooth running of daily lives around the country. In other to realize the purpose of basic and daily use of energy and safer environment; researchers and fields of specialists have actualized methods of maximizing the advantages provided through artificial Neural community Intelligence techniques. This article affords an outline of this network and its application in the strength sector, basically the Nigerian national Grid, studies progress of energy recuperation, such as power failure and load recovery. There may be a great job to obtain automatic restoration in huge effective electricity restoration. The studies makes a specialty of healing of electricity failure in Nigeria with the aid of the usage of artificial neural network. There are many reasons of energy failures in Nigeria power community. Nigerian 330kV network was modeled. The network was modeled in PSAT and the south eastern part was mapped out and modeled with electricity library in SIMULINK. The contemporary side of the network without fault prevalence was provided. The contemporary side for each region after switching of each of the place’s circuit breaker was received and applied in producing the ANN model. ANN model version was applied to the power device version in SIMULINK and used to predict the effect switching places or location of the circuit. A great result was achieved thereafter.

Published in American Journal of Electrical Power and Energy Systems (Volume 12, Issue 3)
DOI 10.11648/j.epes.20231203.11
Page(s) 40-50
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Power Failure, Power Restoration, National Grid System, Artificial Neural Network, Artificial Intelligence

References
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[3] Engr. Oladele Amoda, “Lingering Issues In The Nigeria Power Market”, Eko Electricity Distribution Company Lagos, Nigeria Seminar On Dividends Of Privatization Of Power Sector, September, 2014.
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[6] O I Okoro, E Chikuni, (2007) “Power sector reforms in Nigeria: opportunities and challenges”, Journal of Energy in Southern Africa • Vol 18 No 3 • August 2007.
[7] Onohaebi O. S, “Power Outages in the Nigeria Transmission Grid”, Research Journal of Applied Sciences 4 (1): 1-9, 2009.
[8] Isaac, Adekunle Samuel, Daramola, S. A., Ayokunle Awelewa (2014), “Review of System Collapse Incidences on the 330-kV Nigerian Grid” International jurnal of Engineering Science Invention.
[9] Isdore, O. A., Chisom P. M, Nsan-A. P., Ene-Nte, (2021) “Power System Analysis Toolbox (PSAT) Circuit Design of Nigeria 330Kv 10 Generators 28 Bus Power Network for Transient Stability Simulation” Scientific Research Journal (SCIRJ), Volume IX, Issue I, January 2021, ISSN 2201-2796.
[10] Tao, F., Zhang, H., Liu, A., andNee, A. Y., (2019) “Digital Twin in Industry: State of-the-Art, IEEE Transactions on Industrial Informatics” vol. 15, no. 4, pp. 2405–2415.
[11] You, Yinfeng Zhao, Mirka Mandich, Yi Cui, Hongyu Li, Huangqing Xiao, Summer Fabus, Yu Su, Yilu Liu, Fellow, A Review on Artificial Intelligence for Grid Stability Assessment "Comparative assessment of tactics to improve primary frequency response without curtailing solar output in high photovoltaic interconnection grids,... (2020) IEEE, Haoyu Yuan, Huaiguang Jiang, Jin Tan, and Yingchen Zhang (2020).
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[14] Ya-Jun Fan, Hai-tong Xu, and Zhao-Yu He (2021) Smoothing the output power of a wind energy conversion system using a hybrid nonlinear pitch angle controller saga journal. Volume 40, Issue 2 October 12, 2021.
[15] Brosinsky, C., Westermann, D., and Krebs, R., (2018) “System Control Centers,” 2018 IEEE International Energy Conference (ENERGYCON), pp. 1–6.
[16] Jen-hao T, Wei-Hao H, Shang-wen W. (2014). ‘Automatic and fast faulted line section location method for Distribution Based Power System on Fault indicators. IEEE Transmission on 29 (4): 1653-1662 Doi: 10.11091TPWRS.
[17] Vincent, Nsed Ogar, Sajjad Hussain, Kelum, A. A. Gamage. (2023) The use of artificial neural network for low latency of fault detection and localisation in transmission line Tri Wibawa Heliyon • February 2023 Volume 9, Issue 2, February 2023, e13376.
[18] Zhou, X., H. Wang, Y. Ma, and Z. Gao, (2018) “Research on Power System Restoration Based on Multi-agent Systems,” Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018, pp. 4955–4960.
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[20] Eskandarpour, R.; Khodaei. (2016) ‘A. Machine learning based power grid outage prediction in response to extreme events’IEEETrans. Power Syst., 32, 3315–3316.
[21] Hatziargyriou, N., Milanovic, J. V., Rahmann, C, Ajjarapu, V, Canizares, C., I. Erlich, D. Hill, I. Hiskens, I. Kamwa, B. Pal, P. Pourbeik, J. J. Sanchez-Gasca, A. Stankovic, M, VanCutsem T, V. Vittal, and C. Vournas, (2020), “Definition and Classification of Power System Stability Revisited & Extended,” IEEE Transactions on Power Systems.
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Cite This Article
  • APA Style

    Uchegbu Chinenye Eberechi, Inyama Kelechi, Kalu Peace Onyekachi. (2023). Effective Power Restoration in the National Grid, Using Interconnected System (Neural Network Intelligence): A Review of the Nigerian Grid System. American Journal of Electrical Power and Energy Systems, 12(3), 40-50. https://doi.org/10.11648/j.epes.20231203.11

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    ACS Style

    Uchegbu Chinenye Eberechi; Inyama Kelechi; Kalu Peace Onyekachi. Effective Power Restoration in the National Grid, Using Interconnected System (Neural Network Intelligence): A Review of the Nigerian Grid System. Am. J. Electr. Power Energy Syst. 2023, 12(3), 40-50. doi: 10.11648/j.epes.20231203.11

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    AMA Style

    Uchegbu Chinenye Eberechi, Inyama Kelechi, Kalu Peace Onyekachi. Effective Power Restoration in the National Grid, Using Interconnected System (Neural Network Intelligence): A Review of the Nigerian Grid System. Am J Electr Power Energy Syst. 2023;12(3):40-50. doi: 10.11648/j.epes.20231203.11

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  • @article{10.11648/j.epes.20231203.11,
      author = {Uchegbu Chinenye Eberechi and Inyama Kelechi and Kalu Peace Onyekachi},
      title = {Effective Power Restoration in the National Grid, Using Interconnected System (Neural Network Intelligence): A Review of the Nigerian Grid System},
      journal = {American Journal of Electrical Power and Energy Systems},
      volume = {12},
      number = {3},
      pages = {40-50},
      doi = {10.11648/j.epes.20231203.11},
      url = {https://doi.org/10.11648/j.epes.20231203.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20231203.11},
      abstract = {Power demand is increasing with recent developments in technology to improve and facilitate the smooth running of daily lives around the country. In other to realize the purpose of basic and daily use of energy and safer environment; researchers and fields of specialists have actualized methods of maximizing the advantages provided through artificial Neural community Intelligence techniques. This article affords an outline of this network and its application in the strength sector, basically the Nigerian national Grid, studies progress of energy recuperation, such as power failure and load recovery. There may be a great job to obtain automatic restoration in huge effective electricity restoration. The studies makes a specialty of healing of electricity failure in Nigeria with the aid of the usage of artificial neural network. There are many reasons of energy failures in Nigeria power community. Nigerian 330kV network was modeled. The network was modeled in PSAT and the south eastern part was mapped out and modeled with electricity library in SIMULINK. The contemporary side of the network without fault prevalence was provided. The contemporary side for each region after switching of each of the place’s circuit breaker was received and applied in producing the ANN model. ANN model version was applied to the power device version in SIMULINK and used to predict the effect switching places or location of the circuit. A great result was achieved thereafter.},
     year = {2023}
    }
    

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    AU  - Uchegbu Chinenye Eberechi
    AU  - Inyama Kelechi
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    JO  - American Journal of Electrical Power and Energy Systems
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    AB  - Power demand is increasing with recent developments in technology to improve and facilitate the smooth running of daily lives around the country. In other to realize the purpose of basic and daily use of energy and safer environment; researchers and fields of specialists have actualized methods of maximizing the advantages provided through artificial Neural community Intelligence techniques. This article affords an outline of this network and its application in the strength sector, basically the Nigerian national Grid, studies progress of energy recuperation, such as power failure and load recovery. There may be a great job to obtain automatic restoration in huge effective electricity restoration. The studies makes a specialty of healing of electricity failure in Nigeria with the aid of the usage of artificial neural network. There are many reasons of energy failures in Nigeria power community. Nigerian 330kV network was modeled. The network was modeled in PSAT and the south eastern part was mapped out and modeled with electricity library in SIMULINK. The contemporary side of the network without fault prevalence was provided. The contemporary side for each region after switching of each of the place’s circuit breaker was received and applied in producing the ANN model. ANN model version was applied to the power device version in SIMULINK and used to predict the effect switching places or location of the circuit. A great result was achieved thereafter.
    VL  - 12
    IS  - 3
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Author Information
  • Department of Electrical and Electronic Engineering, Abia State University, Uturu, Nigeria

  • Department of Electrical and Electronic Engineering, Abia State University, Uturu, Nigeria

  • Department of Electrical and Electronic Engineering, Abia State University, Uturu, Nigeria

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