Elektrik Dağıtım Sistemi Verimliliğinin Artırılması: TLBO Algoritması ile İletken Kesit Optimizasyonu için Bir Yazılım Aracı Tasarımı

Yıl 2024, , 41 - 53, 21.06.2024

Cemil Altın

https://doi.org/10.53525/jster.1459185

Öz

Birçok ayrı dal ve bölümden oluşan radyal dağıtım şebekelerinde optimum iletken kesitini hesaplamak çok zordur. Bu çalışma, Öğretme-Öğrenme Tabanlı Optimizasyon (TLBO) algoritmasını kullanarak elektrik dağıtım sistemlerinde iletken kesit alanını optimize etmek için yeni bir araç sunmaktadır. Araç, çok kesitli, dallanan dağıtım sistemlerinin iletken kesitini optimize etmektedir. Her bir şebeke segmenti için ideal iletken boyutunu seçmek üzere amaç fonksiyonu formüle edilirken maksimum akım taşıma kapasitesi kısıtı dikkate alınmıştır. Optimum iletken kesitleri, hem istenen yüzde gerilim düşümü hem de iletkenin akım taşıma kapasitesi ile belirlenmektedir. Hat segmentlerinden çekilen akımlar önceden hesaplanarak optimizasyon algoritmasının arama uzayı daraltılmıştır. İdeal iletken kesitlerini belirlemek için MATLAB ve Excel birlikte kullanılmaktadır. Önerilen yöntem kullanılarak seçilen iletken, radyal dağıtım sistemlerinde uygun gerilim seviyelerini korurken, iletken malzeme ve enerji kaybı maliyetlerindeki toplam tasarrufu dolaylı olarak optimize etmektedir. Sonuçlar, optimum iletken seçimi probleminin TLBO algoritması ile pratik ve etkili bir şekilde çözülebileceğini göstermektedir. Önerilen aracın bir radyal dağıtım sistemi üzerinde test edilmesinin sonuçları dikkate değerdir.

Anahtar Kelimeler

Dağıtım Şebekeleri, TLBO, Metasezgisel, İletken optimizasyonu

Proje Numarası

No project

Enhancing Electrical Distribution System Efficiency: A Software Tool Design for Conductor Cross-Section Optimization With TLBO Algorithm

Öz

In radial distribution networks with many separate branches and sections, it is very difficult to calculate the optimum conductor cross-section. This work introduces a new tool for optimizing conductor cross-sectional area in electrical distribution systems by utilizing the Teaching-Learning-Based Optimization (TLBO) algorithm. The tool can optimize the conductor crossection of the multisection, branching distribution systems. The maximum current carrying capacity constraint is taken into consideration when formulating the objective function to choose the ideal conductor size for each network segment. The optimal conductor sizes are determined by both desired percent voltage drop and current carrying capacity of the conductor. By calculating the currents drawn from the line segments in advance, the search space of the optimization algorithm is narrowed. MATLAB and Excel were used to determine the ideal conductor size. The conductor, which is chosen using the suggested method, will preserve appropriate voltage levels in radial distribution systems while optimizing the overall savings in conducting material and energy loss costs. The outcomes show that the optimal selection of conductor problem can be solved by the TLBO algorithm in a practical and effective manner. Results of testing the suggested tool on a radial distribution system are noteworthy.

Anahtar Kelimeler

Distribution Networks, TLBO, Metaheuristics, Conductor Optimization

Etik Beyan

In the studies carried out within the scope of this article, the rules of research and publication ethics were followed.

Destekleyen Kurum

No institution

Proje Numarası

No project

Teşekkür

No thanks

Kaynakça

• [1] Z. Wang, H. Liu, D. C. Yu, X. Wang, and H. Song, “A practical approach to the conductor size selection in planning radial distribution systems,” IEEE Trans. Power Deliv., vol. 15, no. 1, pp. 350–354, 2000, doi: 10.1109/61.847272.
• [2] S. Sivanagaraju, N. Sreenivasulu, M. Vijayakumar, and T. Ramana, “Optimal conductor selection for radial distribution systems,” Electr. Power Syst. Res., vol. 63, no. 2, pp. 95–103, 2002, doi: 10.1016/S0378-7796(02)00081-0.
• [3] L. A. Gallego Pareja, J. M. López-Lezama, and O. Gómez Carmona, “A MILP Model for Optimal Conductor Selection and Capacitor Banks Placement in Primary Distribution Systems,” Energies, vol. 16, no. 11, pp. 1–21, 2023, doi: 10.3390/en16114340.
• [4] D. Joshi, S. Burada, and K. D. Mistry, “Distribution system planning with optimal conductor selection,” 2017 Recent Dev. Control. Autom. Power Eng. RDCAPE 2017, vol. 3, pp. 263–268, 2018, doi: 10.1109/RDCAPE.2017.8358279.
• [5] M. Ramalinga Raju, K. V. S. Ramachandra Murthy, K. Ravindra, and R. Srinivasa Rao, “Optimal conductor selection for agricultural distribution system - A case study,” 2010 Int. Conf. Intell. Adv. Syst. ICIAS 2010, pp. 1–6, 2010, doi: 10.1109/ICIAS.2010.5716178.
• [6] R. Ranjan, B. Venkatesh, and D. Das, “Optimal conductor selection of radial distribution networks using fuzzy adaptation of evolutionary programming,” Int. J. Power Energy Syst., vol. 26, no. 3, pp. 226–232, 2006, doi: 10.2316/journal.203.2006.3.203-3444.
• [7] M. Kumari, V. R. Singh, and R. Ranjan, “Optimal selection of conductor in RDS considering weather condition,” 2018 Int. Conf. Comput. Power Commun. Technol. GUCON 2018, pp. 647–651, 2019, doi: 10.1109/GUCON.2018.8675051.
• [8] M. Waseem, R. Khan, M. Zakria, S. Jamal, and S. Perveen, “Optimized Cable Sizing-An Economical Approach to Energy Saving with Reduced Power Loss,” 4th Int. Conf. Power Gener. Syst. Renew. Energy Technol. PGSRET 2018, no. September, pp. 1–4, 2019, doi: 10.1109/PGSRET.2018.8685987.
• [9] F. Mendoza, D. Requena, J. L. Bemal-Agustín, and J. A. Domínguez-Navarro, “Optimal conductor size selection in radial power distribution systems using evolutionary strategies,” 2006 IEEE PES Transm. Distrib. Conf. Expo. Lat. Am. TDC’06, 2006, doi: 10.1109/TDCLA.2006.311451.
• [10] S. Sivanagaraju and J. V. Rao, “Optimal conductor selection in radial distribution system using discrete Particle Swarm Optimization,” UK World J. Model. Simul., vol. 1, no. 3, pp. 183–191, 2009.
• [11] R. Srinivasa Rao, “Optimal Conductor Selection For Loss Reduction In Radial Distribution Systems Using Differential Evolution,” Int. J. Eng. Sci. Technol., vol. 2, no. 7, pp. 2829–2838, 2010.
• [12] M. M. Legha, F. Ostovar, and M. M. Legha, “Combination of Optimal Conductor Selection and Capacitor Placement in Radial Distribution Systems Using PSO Method,” Iraq J. Electr. Electron. Eng., vol. 10, no. 1, pp. 33–41, 2014, doi: 10.33762/EEEJ.2014.93016.
• [13] P. Samal, S. Mohanty, and S. Ganguly, “Simultaneous capacitor allocation and conductor sizing in unbalanced radial distribution systems using differential evolution algorithm,” 2016 Natl. Power Syst. Conf. NPSC 2016, Feb. 2017, doi: 10.1109/NPSC.2016.7858853.
• [14] H. Samet and M. M. Legha, “Optimal Conductor Selection in radial Distribution Using Imperialism Competitive Algorithm and Comparison with PSO,” 6th Int. Conf. from "Scientific to Comput. Eng., no. July, pp. 1–9, 2014.
• [15] T. M. Khalil and A. V. Gorpinich, “Optimal conductor selection and capacitor placement for loss reduction of radial distribution systems by selective particle swarm optimization,” Proc. - ICCES 2012 2012 Int. Conf. Comput. Eng. Syst., pp. 215–220, 2012, doi: 10.1109/ICCES.2012.6408516.
• [16] A. Y. Abdelaziz and A. Fathy, “A novel approach based on crow search algorithm for optimal selection of conductor size in radial distribution networks,” Eng. Sci. Technol. an Int. J., vol. 20, no. 2, pp. 391–402, 2017, doi: 10.1016/j.jestch.2017.02.004.
• [17] J. F. Martínez-Gil, N. A. Moyano-García, O. D. Montoya, and J. A. Alarcon-Villamil, “Optimal selection of conductors in three-phase distribution networks using a discrete version of the vortex search algorithm,” Computation, vol. 9, no. 7, 2021, doi: 10.3390/computation9070080.
• [18] G. Bayrak, “Elektrik Tesis Projesi Ders Notları.” Accessed: Mar. 18, 2024. [Online]. Available: https://docplayer.biz.tr/4552727-Elektrik-tesis-projesi.html
• [19] R. V. Rao, V. J. Savsani, and D. P. Vakharia, “Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems,” CAD Comput. Aided Des., vol. 43, no. 3, pp. 303–315, 2011, doi: 10.1016/j.cad.2010.12.015.