Research Article
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Cost optimization of oil type distribution transformer using multi-objective genetic algorithm

Year 2024, Volume: 8 Issue: 1, 53 - 62, 31.03.2024

Simay Telli İres İskender Emir Yükselen

https://doi.org/10.30521/jes.1383567

Abstract

The demand for electrical energy is increasing day by day with the development of technology in the world. Distributing electric energy to all regions that need energy is a principal issue, and this necessitates the use of transformers to convert the voltage to the desired level. Accordingly, the use of transformers, one of the electrical devices converting AC voltage level at a defined frequency has grown significantly. In this study, design parameters of a 25 kVA, 33/0.4 kV, Yzn11, oil-type distribution transformer are optimized using the Multi-Objective Genetic Algorithm (MOGA) technique by decreasing the weight of the significant materials and manufacturing cost. Electromagnetic analysis of the transformer is performed with ANSYS Maxwell based on the design results obtained from the optimization study for validation of the method. The experimental design parameters are also compared with the optimization results. It is observed that optimum results are achieved by using the proposed approach.

Keywords

Cost Optimization Finite Element Method Multi-Objective Genetic Algorithm Transformer Design

References

  • [1] Chapman Stephen J. Electric Machinery Fundamentals 4th Edition. New York, ABD: McGraw-Hill, 2005.
  • [2] Kül, S, Celtek, S.A, İskender, İ. Metaheuristic Algorithms Based Approaches for Efficiency Analysis of Three Phase Dry-Type Transformers. Konya Journal of Engineering Sciences 2021; 9(4): 889-903. DOI: 10.36306/konjes.946496
  • [3] Smolka, J, Nowak, A. J. Shape optimization of coils and cooling ducts in dry-type transformers using computational fluid dynamics and genetic algorithm. IEEE Transactions on Magnetics 2011; 47(6): 1726-1731. DOI: 10.1109/TMAG.2011.2109731
  • [4] Hernandez, C, Lara, J, Arjona, M.A, Melgoza-Vazquez E. Multi-Objective Electromagnetic Design Optimization of a Power Transformer Using 3D Finite Element Analysis, Response Surface Methodology, and the Third Generation Non-Sorting Genetic Algorithm. Energies 2023; 16(5): 2248. DOI: 10.3390/en16052248
  • [5] Akdağ, M, Çelebi, M. The Weight Optimization of Oil-Type Transformer with Firefly Algorithm. Dicle University Journal of Engineering (DUJE) 2022; 13(2): 169-180. DOI: 10.24012/dumf.1075008
  • [6] Orosz, T, Borbély, B, Tamus, Z. Á. Performance Comparison of Multi Design Method and Meta-Heuristic Methods for Optimal Preliminary Design of Core-Form Power Transformers. Periodica Polytechnica Electrical Engineering and Computer Science 2017; 61(1): 69-76. DOI: 10.3311/PPee.10207
  • [7] Orosz, T, Pánek, D, Karban, P. FEM Based Preliminary Design Optimization in case of Large Power Transformers. Applied Sciences 2020; 10(4): 1361. DOI: 10.3390/app10041361
  • [8] Soldoozy, A, Esmaeli, A, Akbari, H, Mazloom, S. Implementation of Tree Pruning Method for Power Transformer Design Optimization. International Transactions on Electrical Energy Systems 2018; 29(1): 1-19, DOI:10.1002/etep.2659
  • [9] Alonso, B, Meana-Fernández, A, Oro, F. Thermal Response and Failure Mode Evaluation of a Dry-Type Transformer. Applied Thermal Engineering 2017; 120: 763-771, DOI: 10.1016/j.applthermaleng.2017.04.007
  • [10] Özüpak, Y, Mamiş, S. Thermal Field Analysis of Power Transformer by Combined Electromechanical Finite Element Method. Erzincan University Journal of Science and Technology 2019; 12(2): 934-941, DOI:10.18185/erzifbed.513969
  • [11] Yükselen, E., İskender, İ. Case Study on Thermal Optimization of Oil Immersed Transformer Used in Solar Power Plant based on Genetic Algorithm and Computational Fluid Dynamics. Thermal Science 2023; 1(1): 51-64, DOI: 10.2298/TSCI221109051Y
  • [12] Dasgupta I. Power Transformer Quality Assurance. New Delhi, INDIA: New Age International (P) Ltd, 2009
  • [13] Mehta, H. D., Patel Rajesh M. A Review on Transformer Design Optimization and Performance Analysis Using Artificial Intelligence Techniques. International Journal of Science and Research (IJSR) 2014; 3(9): 726-733.
  • [14] Mitchell M. An Introduction to Genetic Algorithms. London, ENGLAND: MIT Press, 1999.
  • [15] Gao, Y, Shi, L, Yao, P. Study on Multi-Objective Genetic Algorithm. Proceedings of the 3rd World Congress on Intelligent Control and Automation 2000; 1:646-650. DOI: 10.1109/WCICA.2000.860052.
  • [16] Phaengkieo, D, Ruangsinchaiwanich, S. Design Optimization of Electrical Transformer Using Artificial Intelligence Techniques. In: 2015 18th International Conference on Electrical Machines and Systems (ICEMS); 25-28 October 2015: IEEE, Pattaya, Thailand: pp. 1381-1385.
  • [17] Forrest, S, Mitchell M. What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation. Machine Learning 1993; 13: 285–319. DOI: 10.1023/A:1022626114466
  • [18] Nair K.R.M. Power and Distribution Transformers Practical Design Guide. England: CRC Press, 2021.
  • [19] Yükselen, E, İskender, İ. Analysis and Design of a Special Type Power Transformer Used in Solar Power Plants. International Journal of Renewable Energy Research (IJRER) 2022; 12(2): 667-673. DOI: 10.20508/ijrer.v12i2.12857.g8454.

Year 2024, Volume: 8 Issue: 1, 53 - 62, 31.03.2024

Simay Telli İres İskender Emir Yükselen

https://doi.org/10.30521/jes.1383567

Abstract

References

  • [1] Chapman Stephen J. Electric Machinery Fundamentals 4th Edition. New York, ABD: McGraw-Hill, 2005.
  • [2] Kül, S, Celtek, S.A, İskender, İ. Metaheuristic Algorithms Based Approaches for Efficiency Analysis of Three Phase Dry-Type Transformers. Konya Journal of Engineering Sciences 2021; 9(4): 889-903. DOI: 10.36306/konjes.946496
  • [3] Smolka, J, Nowak, A. J. Shape optimization of coils and cooling ducts in dry-type transformers using computational fluid dynamics and genetic algorithm. IEEE Transactions on Magnetics 2011; 47(6): 1726-1731. DOI: 10.1109/TMAG.2011.2109731
  • [4] Hernandez, C, Lara, J, Arjona, M.A, Melgoza-Vazquez E. Multi-Objective Electromagnetic Design Optimization of a Power Transformer Using 3D Finite Element Analysis, Response Surface Methodology, and the Third Generation Non-Sorting Genetic Algorithm. Energies 2023; 16(5): 2248. DOI: 10.3390/en16052248
  • [5] Akdağ, M, Çelebi, M. The Weight Optimization of Oil-Type Transformer with Firefly Algorithm. Dicle University Journal of Engineering (DUJE) 2022; 13(2): 169-180. DOI: 10.24012/dumf.1075008
  • [6] Orosz, T, Borbély, B, Tamus, Z. Á. Performance Comparison of Multi Design Method and Meta-Heuristic Methods for Optimal Preliminary Design of Core-Form Power Transformers. Periodica Polytechnica Electrical Engineering and Computer Science 2017; 61(1): 69-76. DOI: 10.3311/PPee.10207
  • [7] Orosz, T, Pánek, D, Karban, P. FEM Based Preliminary Design Optimization in case of Large Power Transformers. Applied Sciences 2020; 10(4): 1361. DOI: 10.3390/app10041361
  • [8] Soldoozy, A, Esmaeli, A, Akbari, H, Mazloom, S. Implementation of Tree Pruning Method for Power Transformer Design Optimization. International Transactions on Electrical Energy Systems 2018; 29(1): 1-19, DOI:10.1002/etep.2659
  • [9] Alonso, B, Meana-Fernández, A, Oro, F. Thermal Response and Failure Mode Evaluation of a Dry-Type Transformer. Applied Thermal Engineering 2017; 120: 763-771, DOI: 10.1016/j.applthermaleng.2017.04.007
  • [10] Özüpak, Y, Mamiş, S. Thermal Field Analysis of Power Transformer by Combined Electromechanical Finite Element Method. Erzincan University Journal of Science and Technology 2019; 12(2): 934-941, DOI:10.18185/erzifbed.513969
  • [11] Yükselen, E., İskender, İ. Case Study on Thermal Optimization of Oil Immersed Transformer Used in Solar Power Plant based on Genetic Algorithm and Computational Fluid Dynamics. Thermal Science 2023; 1(1): 51-64, DOI: 10.2298/TSCI221109051Y
  • [12] Dasgupta I. Power Transformer Quality Assurance. New Delhi, INDIA: New Age International (P) Ltd, 2009
  • [13] Mehta, H. D., Patel Rajesh M. A Review on Transformer Design Optimization and Performance Analysis Using Artificial Intelligence Techniques. International Journal of Science and Research (IJSR) 2014; 3(9): 726-733.
  • [14] Mitchell M. An Introduction to Genetic Algorithms. London, ENGLAND: MIT Press, 1999.
  • [15] Gao, Y, Shi, L, Yao, P. Study on Multi-Objective Genetic Algorithm. Proceedings of the 3rd World Congress on Intelligent Control and Automation 2000; 1:646-650. DOI: 10.1109/WCICA.2000.860052.
  • [16] Phaengkieo, D, Ruangsinchaiwanich, S. Design Optimization of Electrical Transformer Using Artificial Intelligence Techniques. In: 2015 18th International Conference on Electrical Machines and Systems (ICEMS); 25-28 October 2015: IEEE, Pattaya, Thailand: pp. 1381-1385.
  • [17] Forrest, S, Mitchell M. What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation. Machine Learning 1993; 13: 285–319. DOI: 10.1023/A:1022626114466
  • [18] Nair K.R.M. Power and Distribution Transformers Practical Design Guide. England: CRC Press, 2021.
  • [19] Yükselen, E, İskender, İ. Analysis and Design of a Special Type Power Transformer Used in Solar Power Plants. International Journal of Renewable Energy Research (IJRER) 2022; 12(2): 667-673. DOI: 10.20508/ijrer.v12i2.12857.g8454.
There are 19 citations in total.

Details

Primary Language English
Subjects Electrical Energy Storage, Electrical Energy Transmission, Networks and Systems, Electrical Energy Generation (Incl. Renewables, Excl. Photovoltaics)
Journal Section Research Articles
Authors

Simay Telli 0009-0009-9964-1994

İres İskender 0000-0003-1968-1857

Emir Yükselen 0000-0002-4364-9665

Early Pub Date March 16, 2024
Publication Date March 31, 2024
Submission Date November 1, 2023
Acceptance Date February 26, 2024
Published in Issue Year 2024 Volume: 8 Issue: 1

Cite

Vancouver Telli S, İskender İ, Yükselen E. Cost optimization of oil type distribution transformer using multi-objective genetic algorithm. JES. 2024;8(1):53-62.
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