Dynamic Modeling and Simulation of Wind Turbine Unit Primary Frequency Regulation Considering Multi-domain Coupling Characteristics
During the rapid frequency regulation process of wind turbine units, the transient active power release can induce load fluctuations in aerodynamic, transmission, and tower components. In order to reasonably characterize the fluctuation characteristics and serve the optimization of frequency regulat...
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State Grid Energy Research Institute
2025-05-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202411044 |
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| author | Zhanyang JI Yang HU Lingxing KONG Ziqiu SONG Dan DENG Jizhen LIU |
| author_facet | Zhanyang JI Yang HU Lingxing KONG Ziqiu SONG Dan DENG Jizhen LIU |
| author_sort | Zhanyang JI |
| collection | DOAJ |
| description | During the rapid frequency regulation process of wind turbine units, the transient active power release can induce load fluctuations in aerodynamic, transmission, and tower components. In order to reasonably characterize the fluctuation characteristics and serve the optimization of frequency regulation control, this paper presents a fast dynamic modeling method for wind turbine units that takes into account the coupling characteristics of blades, main shaft, generator, and control systems. Firstly, a wind farm-turbine coordinated primary frequency regulation control strategy is set up, and a rapid frequency regulation controller at the unit level is developed for both below and above the rated wind speed based on a refined 5MW wind turbine model. And then, the Spilman correlation analysis algorithm is used to select input and output variables with consideration of input and output delay orders, and the operational domain partitioning is completed, enabling adaptive identification and switching between operation regions both above and below the rated wind speed. Thirdly, based on balanced sampling of simulation operating data under discrete operating conditions, and guided by physical prior information, subspace identification and deep neural network algorithms are employed to conduct multi-input-multi-output modeling and simulation verification of the unit's primary frequency modulation dynamics across the full range of operating conditions. The results show that the state space model obtained has good interpretability, but the model structure inherently limits its approximation accuracy to a finite degree; in comparison, the temporal neural network model demonstrates a better ability to capture dynamic characteristics, providing a robust model foundation for subsequent optimization control of the unit's primary frequency modulation. |
| format | Article |
| id | doaj-art-b3c29c75246140f3b0ae244df7d1feaf |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2025-05-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-b3c29c75246140f3b0ae244df7d1feaf2025-08-20T03:11:17ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492025-05-01584566710.11930/j.issn.1004-9649.202411044zgdl-58-04-jizhanyangDynamic Modeling and Simulation of Wind Turbine Unit Primary Frequency Regulation Considering Multi-domain Coupling CharacteristicsZhanyang JI0Yang HU1Lingxing KONG2Ziqiu SONG3Dan DENG4Jizhen LIU5State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing 102206, ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing 102206, ChinaChina Electric Power Research Institute, Beijing 100192, ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing 102206, ChinaSchool of Control and Computer Engineering, North China Electric Power University, Beijing 102206, ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing 102206, ChinaDuring the rapid frequency regulation process of wind turbine units, the transient active power release can induce load fluctuations in aerodynamic, transmission, and tower components. In order to reasonably characterize the fluctuation characteristics and serve the optimization of frequency regulation control, this paper presents a fast dynamic modeling method for wind turbine units that takes into account the coupling characteristics of blades, main shaft, generator, and control systems. Firstly, a wind farm-turbine coordinated primary frequency regulation control strategy is set up, and a rapid frequency regulation controller at the unit level is developed for both below and above the rated wind speed based on a refined 5MW wind turbine model. And then, the Spilman correlation analysis algorithm is used to select input and output variables with consideration of input and output delay orders, and the operational domain partitioning is completed, enabling adaptive identification and switching between operation regions both above and below the rated wind speed. Thirdly, based on balanced sampling of simulation operating data under discrete operating conditions, and guided by physical prior information, subspace identification and deep neural network algorithms are employed to conduct multi-input-multi-output modeling and simulation verification of the unit's primary frequency modulation dynamics across the full range of operating conditions. The results show that the state space model obtained has good interpretability, but the model structure inherently limits its approximation accuracy to a finite degree; in comparison, the temporal neural network model demonstrates a better ability to capture dynamic characteristics, providing a robust model foundation for subsequent optimization control of the unit's primary frequency modulation.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202411044wind turbine unitprimary frequency regulationload dynamicslstm neural networksubspace identification |
| spellingShingle | Zhanyang JI Yang HU Lingxing KONG Ziqiu SONG Dan DENG Jizhen LIU Dynamic Modeling and Simulation of Wind Turbine Unit Primary Frequency Regulation Considering Multi-domain Coupling Characteristics Zhongguo dianli wind turbine unit primary frequency regulation load dynamics lstm neural network subspace identification |
| title | Dynamic Modeling and Simulation of Wind Turbine Unit Primary Frequency Regulation Considering Multi-domain Coupling Characteristics |
| title_full | Dynamic Modeling and Simulation of Wind Turbine Unit Primary Frequency Regulation Considering Multi-domain Coupling Characteristics |
| title_fullStr | Dynamic Modeling and Simulation of Wind Turbine Unit Primary Frequency Regulation Considering Multi-domain Coupling Characteristics |
| title_full_unstemmed | Dynamic Modeling and Simulation of Wind Turbine Unit Primary Frequency Regulation Considering Multi-domain Coupling Characteristics |
| title_short | Dynamic Modeling and Simulation of Wind Turbine Unit Primary Frequency Regulation Considering Multi-domain Coupling Characteristics |
| title_sort | dynamic modeling and simulation of wind turbine unit primary frequency regulation considering multi domain coupling characteristics |
| topic | wind turbine unit primary frequency regulation load dynamics lstm neural network subspace identification |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202411044 |
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