Optimization of Dry-Type Transformer Parameters with Different Methods and FEA Analysis

Year 2022, , 701 - 704, 31.03.2022

Seda Kül , Suleyman Sungur Tezcan , Huseyin Duysak , Seyit Alperen Celtek

https://doi.org/10.31590/ejosat.1084380

Abstract

Due to the importance of correct optimization of transformer design parameters and efficiency, six design variables are used in this study for the optimization of a dry type three-phase transformer based on FEA analysis. Optimization was carried out using the variables of an iron cross-section acceptability (C), the current density of primary and secondary windings (s), magnetic flux density (B), and primary and secondary windings cross-section area (q1, q2). For efficiency optimization, particle swarm optimization (PSO) and Artificial Bee Colony (ABC) algorithms are used and magnetic flux distribution and loss values are obtained with ANSYS/MAXWELL. As a result of the optimization, 98.67% and 98.69% efficiency, 1096.56 and 1108.45 W power gains were obtained with PSO and ABC. In addition, the change in magnetic flux distribution according to the cross-sectional area determined according to the C value obtained as a result of the optimization is shown.

Keywords

Power transformer, optimization, PSO, Artificial bee colony, FEA

Optimization of Dry-Type Transformer Parameters with Different Methods and FEA Analysis

Abstract

Trafo tasarım parametrelerinin ve verimliliğin doğru optimizasyonunun öneminden dolayı, bu çalışmada, FEA analizine dayalı olarak kuru tip üç fazlı bir transformatörün optimizasyonu için altı tasarım değişkeni kullanılmıştır. Optimizasyon işlemi, bir demir kesit kabul edilebilirliği (C), birincil ve ikincil sargıların (s) akım yoğunluğu, manyetik akı yoğunluğu (B) ve birincil ve ikincil sargı kesit alanı (q1, q2) değişkenleri kullanılarak gerçekleştirilmiştir. Verimlilik optimizasyonu için partikül sürü optimizasyonu (PSO) ve Yapay Arı Kolonisi (ABC) algoritmaları kullanılıp, ANSYS/MAXWELL ile manyetik akı dağılımı ve kayıp değerleri elde edilir. Optimizasyon sonucunda PSO ve ABC ile %98.67 ve %98.69 verim, 1096,56 ve 1108,45 W güç kazancı elde edilmiştir. Ayrıca optimizasyon sonucunda elde edilen C değerine göre belirlenen kesit alanına göre manyetik akı dağılımındaki değişim gösterilmektedir.

Keywords

PSO, Power transformer, optimization, PSO, Artificial bee colony, FEA

References

• Mehta, H. D., & Patel, R. M. (2014). A review on transformer design optimization and performance analysis using artificial intelligence techniques. International Journal of Science and Research, 3(9), 726-733.
• Rodríguez, S., Sánchez, N., & Gómez, D. (2019). Optimization of geometric parameters of power transformer using bee” s algorithm”. Annals of Electrical and Electronic Engineering, 2(7), 7-10.doi: 10.21833/aeee.2019.07.002.
• Aksu, İ. Ö., & Demirdelen, T. (2018). A comprehensive study on dry type transformer design with swarm-based metaheuristic optimization methods for industrial applications. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 40(14), 1743-1752. doi: 10.1080/15567036.2018.1486908.
• Azizian, D., Bigdeli, M., & Faiz, J. (2016). Design optimization of cast-resin transformer using nature-inspired algorithms. Arabian Journal for Science and Engineering, 41(9), 3491-3500. doi: 10.1007/s13369-016-2066-x.
• M. S. Mohammed and R. A. Vural, ‘NSGA-II+FEM Based Loss Optimization of Three-Phase Transformer’, IEEE Trans. Ind. Electron., vol. 66, no. 9, 2019, doi: 10.1109/TIE.2018.2881935.
• Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the sixth international symposium on micro machine and human science (pp. 39-43). Ieee. doi: 10.1109/mhs.1995.494215.
• Çeltek, S. A., & Durdu, A. (2020). An Operant Conditioning Approach For Large Scale Social Optimization Algorithms. Konya Mühendislik Bilimleri Dergisi, 8, 38-45. doi: 10.36306/KONJES.821958.
• Seda, Kul., Celtek, S. A., & İskender, İ. Metaheuristic Algorithms Based Approaches for Efficiency Analysis Of Three-Phase Dry-Type Transformers. Konya Mühendislik Bilimleri Dergisi, 9(4), 889-903. doi: 10.36306/KONJES.946496.
• Latchoumi, T. P., Balamurugan, K., Dinesh, K., & Ezhilarasi, T. P. (2019). Particle swarm optimization approach for waterjet cavitation peening. Measurement, 141, 184-189. doi: 10.1016/j.measurement.2019.04.040.
• Celtek, S. A., Durdu, A., & Alı, M. E. M. (2020). Real-time traffic signal control with swarm optimization methods. Measurement, 166, 108206. doi: 10.1016/j.measurement.2020.108206.
• Yigit, E., & Duysak, H. (2019). Determination of optimal layer sequence and thickness for broadband multilayer absorber design using double-stage artificial bee colony algorithm. IEEE Transactions on Microwave Theory and Techniques, 67(8), 3306-3317. doi: 10.1109/TMTT.2019.2919574.
• Yiğit, E., & Duysak, H. (2020). Fully optimized multilayer radar absorber design using multi-objective abc algorithm. International Journal of Engineering and Geosciences, 6(3), 136-145. doi: 10.26833/ijeg.743661.
• Zhou, X., Gao, F., Fang, X., & Lan, Z. (2021). Improved bat algorithm for UAV path planning in three-dimensional space. IEEE Access, 9, 20100-20116. doi: 10.1109/ACCESS.2021.3054179.
• Ewees, A. A., Abd Elaziz, M., Al-Qaness, M. A., Khalil, H. A., & Kim, S. (2020). Improved artificial bee colony using sine-cosine algorithm for multi-level thresholding image segmentation. Ieee Access, 8, 26304-26315. doi: 10.1109/ACCESS.2020.2971249.
• Karaboga, D., Gorkemli, B., Ozturk, C., & Karaboga, N. (2014). A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42(1), 21-57. doi: 10.1007/s10462-012-9328-0.
• Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459-471. doi: 10.1007/s10898-007-9149-x.
• Hepbaslı, A., Enerji Verimliliği ve Yönetim Sistemi, vol. Schneider Electric. İstanbul: Esen Ofset Yayıncılık, 2010.