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Low Switching Frequency Model Predictive Current Control of DFIG with Inductance Identification Function
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Siyuan HUANG, Shuxi LIU, Ke LIU, Bo TANG, Zihao WANG
Journal of Power Supply | 2026, 24(8) : 89 - 99
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Journal of Power Supply | 2026, 24(8): 89-99
• Renewable Energy Generation and Energy Storage •
Low Switching Frequency Model Predictive Current Control of DFIG with Inductance Identification Function
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Siyuan HUANG, Shuxi LIU, Ke LIU, Bo TANG, Zihao WANG
Affiliations
  • School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China
Published: 2026-08-30 doi: 10.13234/j.issn.2095-2805.2026.8.89
Outline
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Aimed at the problems of poor steady-state performance, high switching loss and susceptibility to changes in motor parameters of duty cycle model predictive current control, the low switching frequency model predictive current control strategy for a doubly-fed induction generator (DFIG) with an inductance identification function is studied. First, according to the principle of deadbeat control of rotor voltage, six expected voltage vectors are synthesized by distributing the action time of two adjacent basic voltage vectors and zero vectors. Second, a value function is constructed by taking the minimum rotor voltage prediction error and the minimum switching times at the switching points of two adjacent control cycles as the constraint conditions, and six vector combinations are enumerated and optimized. Finally, the d axis and q axis stator and rotor flux state equations are established. The model reference adaptive system (MRAS) algorithm is used to identify the inductance parameters in the motor system online, and the real-time identification results are fed back to the system. Simulation and experimental results show that the proposed strategy has good control performance and parameter robustness while effectively reducing the switching frequency.

Model predictive current control  /  inductance identification  /  doubly-fed induction generator (DFIG)  /  low switching frequency  /  model reference adaptive system (MRAS)
黄思源, 刘述喜, 刘科, 唐博, 王子豪. 具有电感辨识功能的双馈感应风力发电机低开关频率模型预测电流控制. 电源学报, 2026 , 24 (8) : 89 -99 . DOI: 10.13234/j.issn.2095-2805.2026.8.89
Siyuan HUANG, Shuxi LIU, Ke LIU, Bo TANG, Zihao WANG. Low Switching Frequency Model Predictive Current Control of DFIG with Inductance Identification Function[J]. Journal of Power Supply, 2026 , 24 (8) : 89 -99 . DOI: 10.13234/j.issn.2095-2805.2026.8.89
Funding
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  • Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202001128)
Appendix
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Year 2026 volume 24 Issue 8
37
4
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Article Info
doi: 10.13234/j.issn.2095-2805.2026.8.89
  • Receive Date：2024-09-05
  • Online Date：2026-10-29
  • Published：2026-08-30
Article Data
Affiliations
History
  • Received：2024-09-05
  • Revised：2024-12-30
  • Accepted：2025-02-05
Funding
Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202001128)
Affiliations
    School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China
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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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