Optimization of Transformer Windings Based on FEA-XGBoost and NSGA-III Algorithm
Power transformers are indispensable components for energy transmission and voltage regulation. Since the leakage impedance affects the short-circuit current, magnetic leakage distribution, and manufacturing cost, the accurate calculation of transformer impedance is vital for transformer design. Gen...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/5514678 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841564481481605120 |
|---|---|
| author | Shi Bai-di Jiang Yong-feng Shang Jing-yu Bao Ye-feng Chen Bing-yan Yang Ke |
| author_facet | Shi Bai-di Jiang Yong-feng Shang Jing-yu Bao Ye-feng Chen Bing-yan Yang Ke |
| author_sort | Shi Bai-di |
| collection | DOAJ |
| description | Power transformers are indispensable components for energy transmission and voltage regulation. Since the leakage impedance affects the short-circuit current, magnetic leakage distribution, and manufacturing cost, the accurate calculation of transformer impedance is vital for transformer design. Generally, leakage impedance is mainly decided by the design parameters of the windings, both analytically and numerically. In most given literature studies, the leakage impedance was optimized and analyzed by adjusting the design parameters of the windings without considering the consequent influence on transformer loss and manufacturing costs. A multiobjective optimization model considering the leakage impedance, manufacturing cost, and operating loss of the windings is presented in this paper. The eXtreme Gradient Boosting (XGBoost) model is built using 2048 finite-element analysis (FEA) samples and utilized as a leakage impedance predictor. XGBoost shows better accuracy and consumes much less time compared to analytical and FEA methods. Subsequently, the presented multiobjective model is optimized using multiobjective algorithms and the NSGA-III shows the best performance among the NSGA-II, MOPSO, and MODE. The results show that the leakage impedance is closer to the required value. Besides, the winding manufacturing cost and loss are meanly decreased by 4.7% and 4.1%, respectively, in the engineering case. |
| format | Article |
| id | doaj-art-a6bfb7d5ad2f4f7a86061a2eacbe8c3e |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-a6bfb7d5ad2f4f7a86061a2eacbe8c3e2025-01-02T22:41:03ZengWileyJournal of Electrical and Computer Engineering2090-01552024-01-01202410.1155/2024/5514678Optimization of Transformer Windings Based on FEA-XGBoost and NSGA-III AlgorithmShi Bai-di0Jiang Yong-feng1Shang Jing-yu2Bao Ye-feng3Chen Bing-yan4Yang Ke5College of Mechanical & Electrical EngineeringCollege of Materials Science & EngineeringCollege of Mechanical & Electrical EngineeringCollege of Materials Science & EngineeringCollege of Materials Science & EngineeringCollege of Materials Science & EngineeringPower transformers are indispensable components for energy transmission and voltage regulation. Since the leakage impedance affects the short-circuit current, magnetic leakage distribution, and manufacturing cost, the accurate calculation of transformer impedance is vital for transformer design. Generally, leakage impedance is mainly decided by the design parameters of the windings, both analytically and numerically. In most given literature studies, the leakage impedance was optimized and analyzed by adjusting the design parameters of the windings without considering the consequent influence on transformer loss and manufacturing costs. A multiobjective optimization model considering the leakage impedance, manufacturing cost, and operating loss of the windings is presented in this paper. The eXtreme Gradient Boosting (XGBoost) model is built using 2048 finite-element analysis (FEA) samples and utilized as a leakage impedance predictor. XGBoost shows better accuracy and consumes much less time compared to analytical and FEA methods. Subsequently, the presented multiobjective model is optimized using multiobjective algorithms and the NSGA-III shows the best performance among the NSGA-II, MOPSO, and MODE. The results show that the leakage impedance is closer to the required value. Besides, the winding manufacturing cost and loss are meanly decreased by 4.7% and 4.1%, respectively, in the engineering case.http://dx.doi.org/10.1155/2024/5514678 |
| spellingShingle | Shi Bai-di Jiang Yong-feng Shang Jing-yu Bao Ye-feng Chen Bing-yan Yang Ke Optimization of Transformer Windings Based on FEA-XGBoost and NSGA-III Algorithm Journal of Electrical and Computer Engineering |
| title | Optimization of Transformer Windings Based on FEA-XGBoost and NSGA-III Algorithm |
| title_full | Optimization of Transformer Windings Based on FEA-XGBoost and NSGA-III Algorithm |
| title_fullStr | Optimization of Transformer Windings Based on FEA-XGBoost and NSGA-III Algorithm |
| title_full_unstemmed | Optimization of Transformer Windings Based on FEA-XGBoost and NSGA-III Algorithm |
| title_short | Optimization of Transformer Windings Based on FEA-XGBoost and NSGA-III Algorithm |
| title_sort | optimization of transformer windings based on fea xgboost and nsga iii algorithm |
| url | http://dx.doi.org/10.1155/2024/5514678 |
| work_keys_str_mv | AT shibaidi optimizationoftransformerwindingsbasedonfeaxgboostandnsgaiiialgorithm AT jiangyongfeng optimizationoftransformerwindingsbasedonfeaxgboostandnsgaiiialgorithm AT shangjingyu optimizationoftransformerwindingsbasedonfeaxgboostandnsgaiiialgorithm AT baoyefeng optimizationoftransformerwindingsbasedonfeaxgboostandnsgaiiialgorithm AT chenbingyan optimizationoftransformerwindingsbasedonfeaxgboostandnsgaiiialgorithm AT yangke optimizationoftransformerwindingsbasedonfeaxgboostandnsgaiiialgorithm |