Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm
Variations in generator parameters that occur during the operation of a doubly-fed induction wind turbine (DFIG) constitute a significant factor in the degradation of control performance. To address the problem of difficulty in identifying multiple parameters simultaneously in DFIG, a parameter iden...
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| Materiálatiipa: | Artihkal |
| Giella: | English |
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MDPI AG
2023-01-01
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| Ráidu: | Energies |
| Fáttát: | |
| Liŋkkat: | https://www.mdpi.com/1996-1073/16/3/1355 |
| _version_ | 1827760135730102272 |
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| author | Fanjie Yang Yun Zeng Jing Qian Youtao Li Shihao Xie |
| author_facet | Fanjie Yang Yun Zeng Jing Qian Youtao Li Shihao Xie |
| author_sort | Fanjie Yang |
| collection | DOAJ |
| description | Variations in generator parameters that occur during the operation of a doubly-fed induction wind turbine (DFIG) constitute a significant factor in the degradation of control performance. To address the problem of difficulty in identifying multiple parameters simultaneously in DFIG, a parameter identification method depending on the adaptive grey wolf algorithm with an information-sharing search strategy (ISIAGWO) is proposed to solve the problem of low accuracy and slow identification speed of multiple parameters in DFIG. The easily obtainable generator outlet current was selected as the observed quantity, and the trajectory sensitivity analysis was performed on the five electrical parameters of the DFIG to derive its discriminability. Finally, the parameter recognition of the DFIG was carried out using the ISIAGWO algorithm in the MATLAB/Simulink simulation software, applying the proposed calculation functions. The experimental results show that the ISIAGWO algorithm has better identification accuracy, stability, and convergence for DFIG’s generator parameter identification. |
| first_indexed | 2024-03-11T09:46:09Z |
| format | Article |
| id | doaj.art-9716785a57964a5a9de3783d9f247956 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T09:46:09Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-9716785a57964a5a9de3783d9f2479562023-11-16T16:36:19ZengMDPI AGEnergies1996-10732023-01-01163135510.3390/en16031355Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO AlgorithmFanjie Yang0Yun Zeng1Jing Qian2Youtao Li3Shihao Xie4School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaSchool of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaSchool of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaSchool of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaSchool of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaVariations in generator parameters that occur during the operation of a doubly-fed induction wind turbine (DFIG) constitute a significant factor in the degradation of control performance. To address the problem of difficulty in identifying multiple parameters simultaneously in DFIG, a parameter identification method depending on the adaptive grey wolf algorithm with an information-sharing search strategy (ISIAGWO) is proposed to solve the problem of low accuracy and slow identification speed of multiple parameters in DFIG. The easily obtainable generator outlet current was selected as the observed quantity, and the trajectory sensitivity analysis was performed on the five electrical parameters of the DFIG to derive its discriminability. Finally, the parameter recognition of the DFIG was carried out using the ISIAGWO algorithm in the MATLAB/Simulink simulation software, applying the proposed calculation functions. The experimental results show that the ISIAGWO algorithm has better identification accuracy, stability, and convergence for DFIG’s generator parameter identification.https://www.mdpi.com/1996-1073/16/3/1355doubly-fed induction wind turbinetrajectory sensitivityparameter identificationISIAGWO algorithm |
| spellingShingle | Fanjie Yang Yun Zeng Jing Qian Youtao Li Shihao Xie Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm Energies doubly-fed induction wind turbine trajectory sensitivity parameter identification ISIAGWO algorithm |
| title | Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm |
| title_full | Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm |
| title_fullStr | Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm |
| title_full_unstemmed | Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm |
| title_short | Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm |
| title_sort | parameter identification of doubly fed induction wind turbine based on the isiagwo algorithm |
| topic | doubly-fed induction wind turbine trajectory sensitivity parameter identification ISIAGWO algorithm |
| url | https://www.mdpi.com/1996-1073/16/3/1355 |
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