Parameter Identification of DFIG Converter Control System Based on WOA
The converter is an important component of a wind turbine, and its control system has a significant impact on the dynamic output characteristics of the wind turbine. For the double-fed induction generator (DFIG) converter, the control parameter identification method is proposed. In this paper, a det...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/6/2618 |
| _version_ | 1827750040628625408 |
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| author | Youtao Li Yun Zeng Jing Qian Fanjie Yang Shihao Xie |
| author_facet | Youtao Li Yun Zeng Jing Qian Fanjie Yang Shihao Xie |
| author_sort | Youtao Li |
| collection | DOAJ |
| description | The converter is an important component of a wind turbine, and its control system has a significant impact on the dynamic output characteristics of the wind turbine. For the double-fed induction generator (DFIG) converter, the control parameter identification method is proposed. In this paper, a detailed dynamic model of DFIG with the converter is built, and the trajectory sensitivity method is used to study the observation points that are sensitive to the change of control parameters as the observation quantity for control parameter identification; the Whale Optimization Algorithm (WOA) is used to study the converter control system parameters that dominate the output characteristics of DFIG in the dynamic full-process simulation. To validate the proposed method, four classical test functions are used to verify the effectiveness of the algorithm, and the control parameters are identified by setting a three-phase grounded short-circuit fault under maximum power point tracking (MPPT), and the identification results are compared with particle swarm optimization (PSO) and chaotic particle swarm optimization (CPSO) to show the superiority of the proposed method. The final results show that the proposed WOA can identify the control system parameters faster and more accurately. |
| first_indexed | 2024-03-11T06:37:54Z |
| format | Article |
| id | doaj.art-874bf0aabc2f4243bf11e499cb919bd5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T06:37:54Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-874bf0aabc2f4243bf11e499cb919bd52023-11-17T10:48:36ZengMDPI AGEnergies1996-10732023-03-01166261810.3390/en16062618Parameter Identification of DFIG Converter Control System Based on WOAYoutao Li0Yun Zeng1Jing Qian2Fanjie Yang3Shihao Xie4Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaThe converter is an important component of a wind turbine, and its control system has a significant impact on the dynamic output characteristics of the wind turbine. For the double-fed induction generator (DFIG) converter, the control parameter identification method is proposed. In this paper, a detailed dynamic model of DFIG with the converter is built, and the trajectory sensitivity method is used to study the observation points that are sensitive to the change of control parameters as the observation quantity for control parameter identification; the Whale Optimization Algorithm (WOA) is used to study the converter control system parameters that dominate the output characteristics of DFIG in the dynamic full-process simulation. To validate the proposed method, four classical test functions are used to verify the effectiveness of the algorithm, and the control parameters are identified by setting a three-phase grounded short-circuit fault under maximum power point tracking (MPPT), and the identification results are compared with particle swarm optimization (PSO) and chaotic particle swarm optimization (CPSO) to show the superiority of the proposed method. The final results show that the proposed WOA can identify the control system parameters faster and more accurately.https://www.mdpi.com/1996-1073/16/6/2618DFIGparameter identificationconvertersparameter identificationtrajectory sensitivity |
| spellingShingle | Youtao Li Yun Zeng Jing Qian Fanjie Yang Shihao Xie Parameter Identification of DFIG Converter Control System Based on WOA Energies DFIG parameter identification converters parameter identification trajectory sensitivity |
| title | Parameter Identification of DFIG Converter Control System Based on WOA |
| title_full | Parameter Identification of DFIG Converter Control System Based on WOA |
| title_fullStr | Parameter Identification of DFIG Converter Control System Based on WOA |
| title_full_unstemmed | Parameter Identification of DFIG Converter Control System Based on WOA |
| title_short | Parameter Identification of DFIG Converter Control System Based on WOA |
| title_sort | parameter identification of dfig converter control system based on woa |
| topic | DFIG parameter identification converters parameter identification trajectory sensitivity |
| url | https://www.mdpi.com/1996-1073/16/6/2618 |
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