Power transformer optimal design using an innovative heuristic algorithm combined with mixed-integer non-linear programming and FEM technique
Power transformer optimal design (PTOD) is a complex multi-objective optimisation problem. Due to large variables search space, most optimisation methods proposed in literature for PTOD are prone to find local minimum instead of the global one. To prevail over the aforementioned problem, this study...
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| Published in: | IET generation, transmission & distribution Vol. 11; no. 13; pp. 3359 - 3370 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
The Institution of Engineering and Technology
07.09.2017
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| Subjects: | |
| ISSN: | 1751-8687, 1751-8695 |
| Online Access: | Get full text |
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| Summary: | Power transformer optimal design (PTOD) is a complex multi-objective optimisation problem. Due to large variables search space, most optimisation methods proposed in literature for PTOD are prone to find local minimum instead of the global one. To prevail over the aforementioned problem, this study aims to propose a new heuristic algorithm combined with mixed-integer non-linear programming (MINLP), and also, some modifications to conventional PTOD procedure. Some promising features of the proposed method in comparison to the previously proposed methods are (i) it considers more design variables as a gene for transformer constructing chromosome (TCC), (ii) before building the main TCC, it divides the PTOD algorithm into some sub-algorithms and starts to construct some sub-chromosomes with lower number of genes as parts of the main TCC, which allows us to detect improper genes based on a sensitivity analysis and design constraints, (iii) not only it considers technical and consumer constrains, but it also take manufacturing constraints into account, (iv) MINLP is used to find global optimum point, and (v) the validity of obtained optimal solution is further assessed by presenting comprehensive finite-element method (FEM) and experimental result. |
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| ISSN: | 1751-8687 1751-8695 |
| DOI: | 10.1049/iet-gtd.2017.0151 |