| Peer-Reviewed

Improving Loss Minimization in 33kv Power Distribution Network Using Optimized Genetic Algorithm

Received: 3 May 2021     Accepted: 26 May 2021     Published: 9 June 2021
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Abstract

The epileptic power supply from the national grid due to instability is a concern to energy consumer. This instability in power supply experienced in power distribution network could be minimized by introducing Optimized Genetic Algorithm (OGA). It is achieved by characterizing 33KV distribution network, running the load flow of the characterized 33KV distribution network, determining the distribution losses from the load flow. Minimizing the determined losses in 33kv distribution network using (OGA), and designing SIMULINK model for improving loss minimization in 33kv power distribution network using OGA. Finally, validating and justifying the percentage of loss reduction in improving loss minimization in 33kv power distribution network without and with OGA. The results obtained are conventional percentage power loss in 33KV distribution network, 75%, while that when OGA is incorporated in the system is 72.9%. With these results obtained, the percentage improvement in loss reduction in 33KV distribution network when OGA is used is 2.1%. The conventional percentage of power loss in 33KV distribution network is 80%. The percentage power loss in the distribution network now is 72.9%; hence, power loss reduction in distribution network. Unmitigated power loss was 76.7% when OGA is introduced we had 74.63%. The percentage power loss in distribution network in bus 8 is 81.7% while that when OGA is applied is 79.49%. The percentage power loss in bus 9 of 33KV distribution network is 86.7%. Finally, when optimized genetic algorithm is incorporated in the system the percentage power loss in the network was reduced to 84.36%.

Published in International Journal of Systems Engineering (Volume 5, Issue 1)
DOI 10.11648/j.ijse.20210501.15
Page(s) 34-42
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), 2021. Published by Science Publishing Group

Keywords

Improving, Loss Minimization, Power Distribution, Optimized, Genetic Algorithm (OGA)

References
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Cite This Article
  • APA Style

    Ngang Bassey Ngang, Bakare Kazeem, Ugwu Kevin Ikechukwu, Aneke Nnamere Ezekiel. (2021). Improving Loss Minimization in 33kv Power Distribution Network Using Optimized Genetic Algorithm. International Journal of Systems Engineering, 5(1), 34-42. https://doi.org/10.11648/j.ijse.20210501.15

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    ACS Style

    Ngang Bassey Ngang; Bakare Kazeem; Ugwu Kevin Ikechukwu; Aneke Nnamere Ezekiel. Improving Loss Minimization in 33kv Power Distribution Network Using Optimized Genetic Algorithm. Int. J. Syst. Eng. 2021, 5(1), 34-42. doi: 10.11648/j.ijse.20210501.15

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    AMA Style

    Ngang Bassey Ngang, Bakare Kazeem, Ugwu Kevin Ikechukwu, Aneke Nnamere Ezekiel. Improving Loss Minimization in 33kv Power Distribution Network Using Optimized Genetic Algorithm. Int J Syst Eng. 2021;5(1):34-42. doi: 10.11648/j.ijse.20210501.15

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  • @article{10.11648/j.ijse.20210501.15,
      author = {Ngang Bassey Ngang and Bakare Kazeem and Ugwu Kevin Ikechukwu and Aneke Nnamere Ezekiel},
      title = {Improving Loss Minimization in 33kv Power Distribution Network Using Optimized Genetic Algorithm},
      journal = {International Journal of Systems Engineering},
      volume = {5},
      number = {1},
      pages = {34-42},
      doi = {10.11648/j.ijse.20210501.15},
      url = {https://doi.org/10.11648/j.ijse.20210501.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijse.20210501.15},
      abstract = {The epileptic power supply from the national grid due to instability is a concern to energy consumer. This instability in power supply experienced in power distribution network could be minimized by introducing Optimized Genetic Algorithm (OGA). It is achieved by characterizing 33KV distribution network, running the load flow of the characterized 33KV distribution network, determining the distribution losses from the load flow. Minimizing the determined losses in 33kv distribution network using (OGA), and designing SIMULINK model for improving loss minimization in 33kv power distribution network using OGA. Finally, validating and justifying the percentage of loss reduction in improving loss minimization in 33kv power distribution network without and with OGA. The results obtained are conventional percentage power loss in 33KV distribution network, 75%, while that when OGA is incorporated in the system is 72.9%. With these results obtained, the percentage improvement in loss reduction in 33KV distribution network when OGA is used is 2.1%. The conventional percentage of power loss in 33KV distribution network is 80%. The percentage power loss in the distribution network now is 72.9%; hence, power loss reduction in distribution network. Unmitigated power loss was 76.7% when OGA is introduced we had 74.63%. The percentage power loss in distribution network in bus 8 is 81.7% while that when OGA is applied is 79.49%. The percentage power loss in bus 9 of 33KV distribution network is 86.7%. Finally, when optimized genetic algorithm is incorporated in the system the percentage power loss in the network was reduced to 84.36%.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Improving Loss Minimization in 33kv Power Distribution Network Using Optimized Genetic Algorithm
    AU  - Ngang Bassey Ngang
    AU  - Bakare Kazeem
    AU  - Ugwu Kevin Ikechukwu
    AU  - Aneke Nnamere Ezekiel
    Y1  - 2021/06/09
    PY  - 2021
    N1  - https://doi.org/10.11648/j.ijse.20210501.15
    DO  - 10.11648/j.ijse.20210501.15
    T2  - International Journal of Systems Engineering
    JF  - International Journal of Systems Engineering
    JO  - International Journal of Systems Engineering
    SP  - 34
    EP  - 42
    PB  - Science Publishing Group
    SN  - 2640-4230
    UR  - https://doi.org/10.11648/j.ijse.20210501.15
    AB  - The epileptic power supply from the national grid due to instability is a concern to energy consumer. This instability in power supply experienced in power distribution network could be minimized by introducing Optimized Genetic Algorithm (OGA). It is achieved by characterizing 33KV distribution network, running the load flow of the characterized 33KV distribution network, determining the distribution losses from the load flow. Minimizing the determined losses in 33kv distribution network using (OGA), and designing SIMULINK model for improving loss minimization in 33kv power distribution network using OGA. Finally, validating and justifying the percentage of loss reduction in improving loss minimization in 33kv power distribution network without and with OGA. The results obtained are conventional percentage power loss in 33KV distribution network, 75%, while that when OGA is incorporated in the system is 72.9%. With these results obtained, the percentage improvement in loss reduction in 33KV distribution network when OGA is used is 2.1%. The conventional percentage of power loss in 33KV distribution network is 80%. The percentage power loss in the distribution network now is 72.9%; hence, power loss reduction in distribution network. Unmitigated power loss was 76.7% when OGA is introduced we had 74.63%. The percentage power loss in distribution network in bus 8 is 81.7% while that when OGA is applied is 79.49%. The percentage power loss in bus 9 of 33KV distribution network is 86.7%. Finally, when optimized genetic algorithm is incorporated in the system the percentage power loss in the network was reduced to 84.36%.
    VL  - 5
    IS  - 1
    ER  - 

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Author Information
  • Department of Electrical and Electronic Engineering, Faculty of Engineering, Enugu State University of Science and Technology (ESUT), Enugu, Nigeria

  • Department of Electrical and Electronic Engineering, Faculty of Engineering, Enugu State University of Science and Technology (ESUT), Enugu, Nigeria

  • Department of Electrical and Electronic Engineering, Faculty of Engineering, Enugu State University of Science and Technology (ESUT), Enugu, Nigeria

  • Department of Electrical and Electronic Engineering, Faculty of Engineering, Enugu State University of Science and Technology (ESUT), Enugu, Nigeria

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