Enhancing Electrical Distribution System Efficiency: A Software Tool Design for Conductor Cross-Section Optimization With TLBO Algorithm
Abstract
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.
Keywords
Distribution Networks , TLBO , Metaheuristics , Conductor Optimization
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.
