Data-Driven Dynamic Assessment of Wind Farm Frequency Characteristics Based on State Space Mapping
With the integration of large-scale wind turbines (WTs) into grids via electronic interfaces, power systems are suffering from increasingly serious frequency stability risks. Due to the large number of WTs and their complex dynamic characteristics, operators encounter challenges in coordinating sing...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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China electric power research institute
2025-01-01
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| Series: | CSEE Journal of Power and Energy Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10436612/ |
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| _version_ | 1849715886710063104 |
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| author | Jiachen Liu Zhongguan Wang Xiaodi Zang Xialin Li Li Guo Qinglin Meng Chengshan Wang |
| author_facet | Jiachen Liu Zhongguan Wang Xiaodi Zang Xialin Li Li Guo Qinglin Meng Chengshan Wang |
| author_sort | Jiachen Liu |
| collection | DOAJ |
| description | With the integration of large-scale wind turbines (WTs) into grids via electronic interfaces, power systems are suffering from increasingly serious frequency stability risks. Due to the large number of WTs and their complex dynamic characteristics, operators encounter challenges in coordinating single WTs to provide frequency support directly, and it is necessary to assess the primacy frequency regulation (PFR) capability of wind farms. To cope with the problems of solving complexity and incomplete parameters, a data-driven state space mappingbased linear model for wind farms is developed in this paper to assess the maximum PFR capability. With Koopman operator theory (KOT), the proposed method transforms wind farm PFR nonlinear dynamics into a linear lift-dimension algebraic model, which can assess the maximum PFR capability of wind farms based on historical data in real-time. The simulation results demonstrate that the proposed method has the advantages of fast solving, independence on model parameters, and lower training data requirements. |
| format | Article |
| id | doaj-art-98e1fc564ce045c8b129f4207b3cc079 |
| institution | DOAJ |
| issn | 2096-0042 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | China electric power research institute |
| record_format | Article |
| series | CSEE Journal of Power and Energy Systems |
| spelling | doaj-art-98e1fc564ce045c8b129f4207b3cc0792025-08-20T03:13:11ZengChina electric power research instituteCSEE Journal of Power and Energy Systems2096-00422025-01-011131018102910.17775/CSEEJPES.2023.0243010436612Data-Driven Dynamic Assessment of Wind Farm Frequency Characteristics Based on State Space MappingJiachen Liu0Zhongguan Wang1Xiaodi Zang2Xialin Li3Li Guo4Qinglin Meng5Chengshan Wang6School of Electrical and Information Engineering, Tianjin University,Tianjin,China,300072School of Electrical and Information Engineering, Tianjin University,Tianjin,China,300072School of Electrical and Information Engineering, Tianjin University,Tianjin,China,300072School of Electrical and Information Engineering, Tianjin University,Tianjin,China,300072School of Electrical and Information Engineering, Tianjin University,Tianjin,China,300072School of Electrical and Information Engineering, Tianjin University,Tianjin,China,300072School of Electrical and Information Engineering, Tianjin University,Tianjin,China,300072With the integration of large-scale wind turbines (WTs) into grids via electronic interfaces, power systems are suffering from increasingly serious frequency stability risks. Due to the large number of WTs and their complex dynamic characteristics, operators encounter challenges in coordinating single WTs to provide frequency support directly, and it is necessary to assess the primacy frequency regulation (PFR) capability of wind farms. To cope with the problems of solving complexity and incomplete parameters, a data-driven state space mappingbased linear model for wind farms is developed in this paper to assess the maximum PFR capability. With Koopman operator theory (KOT), the proposed method transforms wind farm PFR nonlinear dynamics into a linear lift-dimension algebraic model, which can assess the maximum PFR capability of wind farms based on historical data in real-time. The simulation results demonstrate that the proposed method has the advantages of fast solving, independence on model parameters, and lower training data requirements.https://ieeexplore.ieee.org/document/10436612/Data-drivendroop controlKoopmanstate space mappingwind farm |
| spellingShingle | Jiachen Liu Zhongguan Wang Xiaodi Zang Xialin Li Li Guo Qinglin Meng Chengshan Wang Data-Driven Dynamic Assessment of Wind Farm Frequency Characteristics Based on State Space Mapping CSEE Journal of Power and Energy Systems Data-driven droop control Koopman state space mapping wind farm |
| title | Data-Driven Dynamic Assessment of Wind Farm Frequency Characteristics Based on State Space Mapping |
| title_full | Data-Driven Dynamic Assessment of Wind Farm Frequency Characteristics Based on State Space Mapping |
| title_fullStr | Data-Driven Dynamic Assessment of Wind Farm Frequency Characteristics Based on State Space Mapping |
| title_full_unstemmed | Data-Driven Dynamic Assessment of Wind Farm Frequency Characteristics Based on State Space Mapping |
| title_short | Data-Driven Dynamic Assessment of Wind Farm Frequency Characteristics Based on State Space Mapping |
| title_sort | data driven dynamic assessment of wind farm frequency characteristics based on state space mapping |
| topic | Data-driven droop control Koopman state space mapping wind farm |
| url | https://ieeexplore.ieee.org/document/10436612/ |
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