RAPID ITERATIVE OPTIMIZATION DESIGN OF WIND TURBINE CONVERTER IGBTS BASED ON IMPROVED NSGA-&#x02161; ALGORITHM

Fan Jia; Zhao Feng; Liu Yifan; Yang Fan; Yan Jiquan; Hu Weifei

doi:10.19912/j.0254-0096.tynxb.2024-2348

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (6) : 221-229. DOI: 10.19912/j.0254-0096.tynxb.2024-2348

RAPID ITERATIVE OPTIMIZATION DESIGN OF WIND TURBINE CONVERTER IGBTS BASED ON IMPROVED NSGA-Ⅱ ALGORITHM

Fan Jia¹, Zhao Feng^2,3, Liu Yifan⁴, Yang Fan^2,3, Yan Jiquan^2,3, Hu Weifei^2,3

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Abstract

Insulated-gate bipolar transistor （IGBT） modules, as the core power components of wind turbine power converters, are crucial for achieving high operational efficiency and cost-effectiveness. Optimizing the electrical conductivity and manufacturing cost of these modules is essential. However, the demanding operational environments often cause performance degradation, and traditional design optimization methods incur high computational costs, making rapid iterative optimization difficult. To address these limitations, this study proposes a rapid iterative design optimization method for wind turbine converter IGBTs based on an improved NSGA-Ⅱ algorithm （Non-dominated Sorting Genetic Algorithm Ⅱ）. Firstly, the optimization problem is formulated with electrical conductivity and manufacturing cost as dual objectives. A parametric thermoelectric coupling model is developed to enable accurate and efficient evaluation of conductivity under varying design conditions. To further enhance the optimization process, an improved NSGA-Ⅱ algorithm based on kernel density estimation is proposed, significantly improving computational efficiency and optimization accuracy compared to traditional optimization algorithms. Additionally, a Kriging-based surrogate model is employed to construct high-fidelity mappings between design variables and optimization objectives, thereby reducing computational burdens and enabling rapid iterative optimization. Numerical experiments confirm the effectiveness and robustness of the proposed method, demonstrating reductions in electrical losses of up to 20.00% and decreases in manufacturing costs by as much as 27.63%. This study provides a practical and efficient design framework for IGBT modules, offering valuable insights into multi-objective optimization in the field of power electronics. By integrating advanced optimization algorithms with surrogate modeling, the proposed method addresses key challenges in the design and performance enhancement of wind turbine power systems.

Key words

IGBT / wind turbines / power converters / NSGA-Ⅱ algorithm / kernel density estimation / optimization algorithm

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Fan Jia, Zhao Feng, Liu Yifan, Yang Fan, Yan Jiquan, Hu Weifei. RAPID ITERATIVE OPTIMIZATION DESIGN OF WIND TURBINE CONVERTER IGBTS BASED ON IMPROVED NSGA-Ⅱ ALGORITHM[J]. Acta Energiae Solaris Sinica. 2026, 47(6): 221-229 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2348

References

[1] 卓振宇, 张宁, 谢小荣,等. 高比例可再生能源电力系统关键技术及发展挑战[J]. 电力系统自动化, 2021, 45(9): 171-191.
ZHUO Z Y, ZHANG N, XIE X R,et al.Key technologies and developing challenges of power system with high proportion of renewable energy[J]. Automation of electric power systems, 2021, 45(9): 171-191.
[2] 胡姚刚, 李辉, 廖兴林, 等. 风电变流器IGBT模块有效工作芯片状态评估[J]. 太阳能学报, 2021, 42(5): 373-379.
HU Y G, LI H, LIAO X L,et al.Effective chip number assessment method of igbt module of wind power converter[J]. Acta energiae solaris sinica, 2021, 42(5): 373-379.
[3] 姚芳, 胡洋, 李铮, 等. 风电变流器IGBT瞬态电热特性测试及其应用研究[J]. 太阳能学报, 2018, 39(2): 369-376.
YAO F,HU Y,LI Z,et al.Transient electrothermal characteristic test and application research of IGBT in wind power converter[J]. Acta energiae solaris sinica, 2018, 39(2): 369-376.
[4] 何伟冬, 王学梅. 用于风电的模块化多电平变流器IGBT的定制化设计[J]. 电力工程技术, 2021, 40(4): 10-17.
HE W D,WANG X M.Customized design for IGBTs of modular multilevel converter for wind power system[J]. Electric power engineering technology, 2021, 40(4): 10-17.
[5] 王红庆, 梁媛. 基于NSGA权值修正的MPC方法及其在光氢系统应用[J]. 太阳能学报, 2024, 45(11): 131-140.
WANG H Q,LIANG Y.MPC method based on nsga weight correction and its application in photohydrogen system[J]. Acta energiae solaris sinica, 2024, 45(11): 131-140.
[6] 曹阳, 房永亮, 刘珍, 等. 基于RSM和NSGA-Ⅱ法的风电系统经济优化研究[J]. 太阳能学报, 2022, 43(1): 161-168.
CAO Y,FANG Y L,LIU Z,et al.Multi-objective optimization of wind power systems based on RSM and NSGA-Ⅱ methods[J]. Acta energiae solaris sinica, 2022, 43(1): 161-168.
[7] DEB K, PRATAP A, AGARWAL S,et al.A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J]. IEEE transactions on evolutionary computation, 2002, 6(2): 182-197.
[8] ZHANG Q, LI H.MOEA/D: a multiobjective evolutionary algorithm based on decomposition[J]. IEEE transactions on evolutionary computation, 2007, 11(6): 712-731.
[9] QI Y, MA X, LIU F,et al.MOEA/D with adaptive weight adjustment[J]. Evolutionary computation, 2014, 22(2): 231-264.
[10] ALAVI O, ABDOLLAH M, VIKI A H.Thermal optimization of IGBT modules based on finite element method and particle swarm optimization[J]. Journal of computational electronics, 2017, 16(3): 930-938.
[11] LIU H, XU S L, MA Y,et al.An adaptive Bayesian sequential sampling approach for global metamodeling[J]. Journal of mechanical design, 2015, 138(1): 011404.
[12] HU W F, ZHAO F, DENG X Y,et al.A new sequential sampling method for surrogate modeling based on a hybrid metric[J]. Journal of mechanical design, 2024, 146(6): 061705.
[13] MO S, LU D, SHI X,et al.A Taylor expansion-based adaptive design strategy for global surrogate modeling with applications in groundwater modeling[J]. Water resources research, 2017, 53(12): 10802-10823.
[14] 于梦阁, 潘振宽, 蒋荣超, 等. 基于近似模型的高速列车头型多目标优化设计[J]. 机械工程学报, 2019, 55(24): 178-186.
YU M G, PAN Z K, JIANG R C,et al.Multi-objective optimization design of the high-speed train head based on the approximate model[J]. Jounrnal of mechanical engineering, 2019, 55(24): 178-186.
[15] 赵峰, 胡伟飞, 李光, 等. 基于混合指标自适应采样代理模型的多目标优化设计方法[J]. 机械工程学报, 2024, 60(13): 81-91.
ZHAO F,HU W F,LI G,et al.Multi-objective optimization design method based on a hybrid metric adaptive sampling surrogate model[J]. Jounrnal of mechanical engineering, 2024, 60(13): 81-91.
[16] BEZERRA M, SANTELLI R, OLIVEIRA E,et al.Response surface methodology (RSM) as a tool for optimization in analytical chemistry[J]. Talanta, 2008, 76(5): 965-977.
[17] ZHOU Q, JIANG P, SHAO X,et al.A variable fidelity information fusion method based on radial basis function[J]. Advanced engineering informatics, 2017, 32: 26-39.
[18] KLEIJNEN J P C. Kriging metamodeling in simulation: a review[J]. European journal of operational research, 2009, 192(3): 707-716.
[19] TIAN Y, CHENG R, ZHANG X,et al.PlatEMO: a Matlab platform for evolutionary multi-objective optimization [educational forum][J]. IEEE computational intelligence magazine, 2017, 12(4): 73-87.
[20] VIANA F A C. A tutorial on Latin hypercube design of experiments[J]. Quality and reliability engineering international, 2016, 32(5): 1975-1985.