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Three-Phase Transformer Optimization Based on the Multi-Objective Particle Swarm Optimization and Non-Dominated Sorting Genetic Algorithm-3 Hybrid Algorithm

Author

Listed:
  • Baidi Shi

    (College of Mechanical & Electrical Engineering, Hohai University, Changzhou 213251, China
    Changzhou Xidan Transformer Co., Ltd., Changzhou 213022, China)

  • Liangxian Zhang

    (Changzhou Xidan Transformer Co., Ltd., Changzhou 213022, China)

  • Yongfeng Jiang

    (College of Mechanical & Electrical Engineering, Hohai University, Changzhou 213251, China
    Jiangsu Province Wind Power Structural Research Center, Nanjing 211100, China)

  • Zixing Li

    (Changzhou Xidan Transformer Co., Ltd., Changzhou 213022, China)

  • Wei Xiao

    (Changzhou Xidan Transformer Co., Ltd., Changzhou 213022, China)

  • Jingyu Shang

    (College of Mechanical & Electrical Engineering, Hohai University, Changzhou 213251, China)

  • Xinfu Chen

    (Changzhou Xidan Transformer Co., Ltd., Changzhou 213022, China)

  • Meng Li

    (Changzhou Xidan Transformer Co., Ltd., Changzhou 213022, China)

Abstract

The performance of transformers directly determines the reliability, stability, and economy of the power system. The methodologies of minimizing the transformer manufacturing cost under the premise of ensuring performance is of great significance. This paper presented an innovative multi-objective optimization model to analyze the relationship between design parameters and transformer indicators. In addition, the sensitive analysis is conducted to exploit the interaction relationships between design parameters and targets. The reliability of the model was demonstrated in 50 MVA/110 kV and 63 MVA/110 kV prototypes, compared with the actual material usage, short-circuit impedance, and load loss, and the maximum error is less than 7%. Due to this problem having many optimization objectives and the high dimension of variables, a two-stage algorithm called MOPSO-NSGA3 (multi-objective particle swarm optimization and non-dominated sorting genetic algorithm-3) is presented. MOPSO is used to find non-domain solutions within the search space in the first stage, and the solution will be used as prior knowledge to initialize the population in NSGA3. The result shows that this algorithm can be effectively used in multi-objective optimization tasks and best meets the requirements of transformer designs that minimize the short-circuit deviation, operating loss, and manufacturing costs.

Suggested Citation

  • Baidi Shi & Liangxian Zhang & Yongfeng Jiang & Zixing Li & Wei Xiao & Jingyu Shang & Xinfu Chen & Meng Li, 2023. "Three-Phase Transformer Optimization Based on the Multi-Objective Particle Swarm Optimization and Non-Dominated Sorting Genetic Algorithm-3 Hybrid Algorithm," Energies, MDPI, vol. 16(22), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7575-:d:1279949
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    References listed on IDEAS

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    1. Wu, Xianguo & Li, Xinyi & Qin, Yawei & Xu, Wen & Liu, Yang, 2023. "Intelligent multiobjective optimization design for NZEBs in China: Four climatic regions," Applied Energy, Elsevier, vol. 339(C).
    2. Wang, Yihan & Chen, Chen & Tao, Yuan & Wen, Zongguo & Chen, Bin & Zhang, Hong, 2019. "A many-objective optimization of industrial environmental management using NSGA-III: A case of China’s iron and steel industry," Applied Energy, Elsevier, vol. 242(C), pages 46-56.
    3. Li, Kun & Ding, Yi-Zhe & Ai, Chen & Sun, Hongwei & Xu, Yi-Peng & Nedaei, Navid, 2022. "Multi-objective optimization and multi-aspect analysis of an innovative geothermal-based multi-generation energy system for power, cooling, hydrogen, and freshwater production," Energy, Elsevier, vol. 245(C).
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