Power Transformer Characterization and Design Optimization Environment
Power Transformers are essential part of all AC Power Grids. This work proposes a characterization and design optimization environment for Power Transformers to increase efficiency while decreasing core volume. It utilizes a Finite-Element State-Space models in conjunction with Artificial Neural Net...
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| Veröffentlicht in: | IEEE transactions on magnetics 2023-05, Vol.59 (5), p.1-1 |
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| container_title | IEEE transactions on magnetics |
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| creator | Arkadan, A. A. Gutierrez-McCoy, M.A. |
| description | Power Transformers are essential part of all AC Power Grids. This work proposes a characterization and design optimization environment for Power Transformers to increase efficiency while decreasing core volume. It utilizes a Finite-Element State-Space models in conjunction with Artificial Neural Networks and Particle Swarm Optimization. A case study of a 42 MVA, 118/13.8kV distribution power transformer was conducted, and transformer performance characteristics were compared to test data for verification. Next, the optimization environment was used to decrease the transformer volume and improve its efficiency. In addition, the Taguchi orthogonal arrays method was used to reduce computational costs. |
| doi_str_mv | 10.1109/TMAG.2023.3237750 |
| format | Article |
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A.</au><au>Gutierrez-McCoy, M.A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Power Transformer Characterization and Design Optimization Environment</atitle><jtitle>IEEE transactions on magnetics</jtitle><stitle>TMAG</stitle><date>2023-05-01</date><risdate>2023</risdate><volume>59</volume><issue>5</issue><spage>1</spage><epage>1</epage><pages>1-1</pages><issn>0018-9464</issn><eissn>1941-0069</eissn><coden>IEMGAQ</coden><abstract>Power Transformers are essential part of all AC Power Grids. This work proposes a characterization and design optimization environment for Power Transformers to increase efficiency while decreasing core volume. It utilizes a Finite-Element State-Space models in conjunction with Artificial Neural Networks and Particle Swarm Optimization. A case study of a 42 MVA, 118/13.8kV distribution power transformer was conducted, and transformer performance characteristics were compared to test data for verification. Next, the optimization environment was used to decrease the transformer volume and improve its efficiency. In addition, the Taguchi orthogonal arrays method was used to reduce computational costs.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TMAG.2023.3237750</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-3178-7742</orcidid></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Design Optimization <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Particle Swarm Optimization <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">State Space Models <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Taguchi Orthogonal Arrays Artificial neural networks Computational modeling Design optimization Finite element method Magnetism Mathematical models Optimization Orthogonal arrays Particle swarm optimization Power transformers Taguchi methods Transformers Windings |
| title | Power Transformer Characterization and Design Optimization Environment |
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