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

Baidi Shi, Liangxian Zhang, Yongfeng Jiang (), Zixing Li, Wei Xiao, Jingyu Shang, Xinfu Chen and Meng Li
Additional contact information
Baidi Shi: College of Mechanical & Electrical Engineering, Hohai University, Changzhou 213251, China
Liangxian Zhang: Changzhou Xidan Transformer Co., Ltd., Changzhou 213022, China
Yongfeng Jiang: College of Mechanical & Electrical Engineering, Hohai University, Changzhou 213251, 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

Energies, 2023, vol. 16, issue 22, 1-21

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.

Keywords: transformer optimization; genetic algorithm; sensitive analysis; multi-objective optimization; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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