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 |
Improving, Loss Minimization, Power Distribution, Optimized, Genetic Algorithm (OGA)
| [1] | D. A. Bitew, A. O. Salau and Y. Gebru (2020). |
| [2] | Load flow and contingency analysis for transmission line outage Arch. Electr. Eng., 69 (3), 273. |
| [3] | A. E. B. Abu-Elanien and K. B. Shaban, (2018). Modern network reconfiguration techniques for service restoration in distribution systems: a step to a smarter grid. Alexandria Engineering Journal, 57 (4), pp. 3959-3967. |
| [4] | Gerez, L. I. Silva and E. A. Belati (2019). Distribution network reconfiguration using selective firefly algorithm and a load flow analysis criterion for reducing the search space. IEEE Access, 7, pp. 67874-67888. |
| [5] | Ade-Ikuesan, O. O., Okakwu, I. K. and Osifeko, M. O.,(2018). Investigation of electric power losses on primary distribution feeder: a case study of Sango - Ota distribution company, Ogun State, Nigeria. International Journal of Applied Engineering Research, Volume 13, Number 7, pp. 5000- 5003. |
| [6] | M. Sedighizadeh, G. Shaghaghi-shahr and M. R. Aghamohammadi (2020) A new optimal operation framework for balanced micro grids considering reconfiguration and generation scheduling simultaneously. |
| [7] | International Transactions on Electrical Energy Systems, pp. 1-31. |
| [8] | Juan Andrés Martín García, Antonio José Gil Mena (2013). “Optimal distributed generation location and size utilising a modified teaching–learning based optimization algorithm”, Electrical Power and Energy Systems, vol 50, pp. 65–75. |
| [9] | Aggelos S. Bouhouras, Kallisthenis I. (2016). “Optimal active and reactive nodal power requirements towards loss minimization under reverse power flow constraint defining DG type”, Electrical Power and Energy Systems, vol 78, pp. 445–454. |
| [10] | Mohammad H. and Moradi, S. M (2014). “Multi-objective PFDE algorithm for solving the optimal siting and sizing problem of multiple DG sources”, Electrical Power and Energy Systems, vol 56, pp. 117–126. |
| [11] | Kumar Mahesh, and Perumal N. (2016). “Advanced Pareto Front NonDominated Sorting Multi-Objective Particle Swarm Optimization for Optimal Placement and Sizing of Distributed Generation”, Energies, 9, 982, pp. 2–23. |
| [12] | N. Mohandas, R. and Balamurugan, L. (2015). “Optimal location and sizing of real power DG units to improve the voltage stability in the distribution system utilizing ABC algorithm united with chaos”, Electrical Power and Energy Systems, vol. 66, pp. 41–52. |
| [13] | Xiangang Peng, and Lixiang Lin (2015).“Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem”, Energies, 8, pp. 13641–13659. |
| [14] | Zeng Pin-zhuo, Wang Ke-you, and Li Guo-jie (2017) “Optimization of Distributed Generation Integrated into Micro Grids Considering the Correlation of DGs”, International Journal of Grid Distribution Computing, vol 8, no. 6, pp. 105–116. International Journal of Grid and Distributed Computing Vol. 10, No. 5. |
| [15] | Duong Quoc Hung, and Nadarajah (2013). “Multiple Distributed Generator Placement in Primary Distribution Networks for Loss Reduction”, IEEE Transactions on Industrial Electronics, vol. 60, no. 4, pp. 1700–1708. |
| [16] | Komail, N., Malihe M. and Farsangi (2013) “An Improved Multi-Objective Harmony Search for Optimal Placement of DGs in Distribution Systems”, IEEE Transactions on Smart Grid, vol. 4, no. 1, pp. 557–567. |
| [17] | Qasim K. and, Xiangning, Lin (2016). “Optimal Placement and Capacity of Capacitor Bank in Radial Distribution System”, International Conference on Energy Efficient Technologies for Sustainability (ICEETS) pp. 416–423. |
| [18] | G. Carpinelli, F. and Mottola (2010). “Optimal Allocation of Dispersed Generators, Capacitors and Distributed Energy Storage Systems in Distribution Networks”, Modern Electric Power Systems (2010), pp. 1–6. |
| [19] | Mohsin S. and Ishtiaq Ahmad (2016). “Load Concentration Factor Based Analytical Method for Optimal Placement of Multiple Distribution Generators for Loss Minimization and Voltage Profile Improvement”, Energies, 9, 287, pp. 1–21. |
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
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
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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Download@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}
}
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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