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Model Predictive Control for Wind Turbine Three-level PMSM with Variable Dead-time
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Chuanyu XIONG1, Liping SUN1, Shiying HE  Member, CPSS2, Sirui SHU1, Zhiwei LI1, Dingchen LI3
Journal of Power Supply | 2026, 24(4) : 133 - 140
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Journal of Power Supply | 2026, 24(4): 133-140
• Renewable Energy Generation and Energy Storage •
Model Predictive Control for Wind Turbine Three-level PMSM with Variable Dead-time
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Chuanyu XIONG1, Liping SUN1, Shiying HE  Member, CPSS2, Sirui SHU1, Zhiwei LI1, Dingchen LI3
Affiliations
  • 1.Economic and Technological Research Institute, State Grid Hubei Electric Power Co., Ltd., Wuhan 430000, China
  • 2.Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
  • 3.School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Published: 2026-04-30 doi: 10.13234/j.issn.2095-2805.2026.4.133
Outline
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The permanent magnet synchronous motor (PMSM) is an important component of high-power fan electric independent variable-pitch systems. When PMSMs are driven with a three-level converter, model predictive control will exhibit drawbacks such as a poor steady-state performance and high computational demand. To address these issues, a simplified strategy utilizing variable dead-time is proposed. First, a reference vector is obtained based on deadbeat prediction. Second, the basic vector that has the smallest Euclidean distance to the reference vector is rapidly identified based on the introduction of an algebraic method, and the optimal basic vector is determined in conjunction with the DC-side capacitor voltage. Third, the dead-zone voltage vector corresponding to this optimal basic vector is calculated, and both of them are considered as the output vectors for the next cycle. In addition, the duty cycle that minimizes the current control error (i.e., the optimal dead-time) is calculated. Finally, experimental results demonstrate the effectiveness of the proposed control strategy.

Permanent magnet synchronous motor (PMSM)  /  three-level converter  /  model predictive control  /  computational efficiency
熊川羽, 孙利平, 何诗英中国电源学会会员, 舒思睿, 李智威, 李丁晨. 风机用三电平PMSM变死区时间模型预测控制. 电源学报, 2026 , 24 (4) : 133 -140 . DOI: 10.13234/j.issn.2095-2805.2026.4.133
Chuanyu XIONG, Liping SUN, Shiying HE, Sirui SHU, Zhiwei LI, Dingchen LI. Model Predictive Control for Wind Turbine Three-level PMSM with Variable Dead-time[J]. Journal of Power Supply, 2026 , 24 (4) : 133 -140 . DOI: 10.13234/j.issn.2095-2805.2026.4.133
  • Research and Application of Distributed Energy Intensive Real-time Control Technology for Future Distribution Networks by Economic and Technological Research Institute, State Grid Hubei Electric Power Co., Ltd.(521538220003)
  • National Natural Science Foundation of China(52207034)
  • Natural Science Foundation of Anhui Province(2208085UD08)
  • Plan for Major Provincial Science & Technology Project of Anhui Province(202003a05020019)
Year 2026 volume 24 Issue 4
36
4
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Article Info
doi: 10.13234/j.issn.2095-2805.2026.4.133
  • Receive Date:2024-01-10
  • Online Date:2026-10-29
  • Published:2026-04-30
Article Data
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History
  • Received:2024-01-10
  • Revised:2024-03-02
  • Accepted:2024-03-10
Funding
Research and Application of Distributed Energy Intensive Real-time Control Technology for Future Distribution Networks by Economic and Technological Research Institute, State Grid Hubei Electric Power Co., Ltd.(521538220003)
National Natural Science Foundation of China(52207034)
Natural Science Foundation of Anhui Province(2208085UD08)
Plan for Major Provincial Science & Technology Project of Anhui Province(202003a05020019)
Affiliations
    1.Economic and Technological Research Institute, State Grid Hubei Electric Power Co., Ltd., Wuhan 430000, China
    2.Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
    3.School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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