Modeling and experimental validation of dry-type transformers with multiobjective swarm intelligence-based optimization algorithms for industrial application

• Published in: Neural computing & applications 2022, Vol.34 (2), p.1079-1098
• Main Authors: Demirdelen, Tugce, Esenboga, Burak, Aksu, Inayet Ozge, Ozdogan, Alican, Yavuzdeger, Abdurrahman, Ekinci, Fırat, Tümay, Mehmet
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; CAD; Coils (windings); Computational Biology/Bioinformatics; Computational fluid dynamics; Computational Science and Engineering; Computer aided design; Computer Science; Cost analysis; Data Mining and Knowledge Discovery; Eddy currents; Energy efficiency; Finite element method; High temperature; Image Processing and Computer Vision; Industrial applications; Multiple objective analysis; Optimization; Original Article; Overheating; Probability and Statistics in Computer Science; Production costs; Software; Swarm intelligence; Temperature; Temperature distribution; Thermal analysis; Transformers
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-06447-z

In recent years, the optimum and efficient design of the transformer core and conductive materials is the most significant issues to overcome the high-temperature problems. The temperature increases on the transformer materials are directly related to the energy efficiency of it. The overheating of the core and coils of the transformer reduces the amount of energy to be obtained from the transformer. However, copper, core, eddy current and other losses can be minimized by obtaining an optimum design of the transformer for maximum efficiency. Thus, the transformer life and the energy efficiency to be obtained from the transformer are maximized. The temperature rise and temperature distribution of the windings can be monitored by computer technology and the transformer can be safely overloaded and the production cost can be minimized. Also, the operating life of the transformers can be further increased by specifying hot spot temperatures on the transformer coils and core. In this study, 3 kVA and 5 kVA Dyn 11 connected 380/220-V dry-type transformers are optimized by multiobjective swarm intelligence-based optimization methods. The main contribution of this study is to prevent the overheating of the transformers by reducing the losses in the transformer core and coils and to reduce the costs of the transformer. The thermal and electromagnetic analyses of the transformers are realized by ANSYS/Maxwell software program which utilizes the industry-leading ANSYS/Fluent computational fluid dynamics and finite element method solvers. Finally, the experimental analyses are realized under the loaded conditions for the transformers. The experimental results are verified with the simulation results. The optimization, modeling, thermal/electromagnetic analysis and experimental processes are carried out step by step in this study. The transformer manufacturers will realize the optimum cost, efficiency and thermal analysis before transformers are manufactured.