Distributed Parameter Identification Framework Based on Intelligent Algorithms for Permanent Magnet Synchronous Wind Generator
Parameter identification of a permanent magnet synchronous wind generator (PMSWG) is of great significance for condition monitoring, fault diagnosis, and robust control. However, the conventional multi-parameter identification approach for a PMSWG is plagued by deficiencies, including its sluggish i...
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| Format: | Article |
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MDPI AG
2025-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/3/683 |
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| _version_ | 1850199140625022976 |
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| author | Xiaoxuan Wu De Tian Huiwen Meng Yi Su |
| author_facet | Xiaoxuan Wu De Tian Huiwen Meng Yi Su |
| author_sort | Xiaoxuan Wu |
| collection | DOAJ |
| description | Parameter identification of a permanent magnet synchronous wind generator (PMSWG) is of great significance for condition monitoring, fault diagnosis, and robust control. However, the conventional multi-parameter identification approach for a PMSWG is plagued by deficiencies, including its sluggish identification speed, subpar accuracy, and susceptibility to local optimization. In light of these challenges, this paper proposes a distributed parameter identification framework based on intelligent algorithms. The proposed approach involves the deployment of SSA, DBO, and PSO algorithms, leveraging golden sine ratio and Gaussian variation strategies for multi-parameter optimization and performance enhancement. Second, the optimal solutions of each intelligent algorithm are aggregated to achieve overall optimization performance enhancement. The efficacy of the proposed method is substantiated by a 6 MW PMSWG parameter identification practice simulation result, which demonstrates its superiority. The proposed method was shown to identify parameters more quickly and effectively than the underlying algorithms, which is of great significance for condition monitoring, fault diagnosis, and robust control of the PMSWG. |
| format | Article |
| id | doaj-art-6cfecbfcd594491eb21a4271d45fbbb2 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-6cfecbfcd594491eb21a4271d45fbbb22025-08-20T02:12:41ZengMDPI AGEnergies1996-10732025-02-0118368310.3390/en18030683Distributed Parameter Identification Framework Based on Intelligent Algorithms for Permanent Magnet Synchronous Wind GeneratorXiaoxuan Wu0De Tian1Huiwen Meng2Yi Su3State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, ChinaState Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, ChinaState Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, ChinaState Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, ChinaParameter identification of a permanent magnet synchronous wind generator (PMSWG) is of great significance for condition monitoring, fault diagnosis, and robust control. However, the conventional multi-parameter identification approach for a PMSWG is plagued by deficiencies, including its sluggish identification speed, subpar accuracy, and susceptibility to local optimization. In light of these challenges, this paper proposes a distributed parameter identification framework based on intelligent algorithms. The proposed approach involves the deployment of SSA, DBO, and PSO algorithms, leveraging golden sine ratio and Gaussian variation strategies for multi-parameter optimization and performance enhancement. Second, the optimal solutions of each intelligent algorithm are aggregated to achieve overall optimization performance enhancement. The efficacy of the proposed method is substantiated by a 6 MW PMSWG parameter identification practice simulation result, which demonstrates its superiority. The proposed method was shown to identify parameters more quickly and effectively than the underlying algorithms, which is of great significance for condition monitoring, fault diagnosis, and robust control of the PMSWG.https://www.mdpi.com/1996-1073/18/3/683permanent magnet synchronous wind generatorparameter identificationdistributed frameworkintelligent algorithms |
| spellingShingle | Xiaoxuan Wu De Tian Huiwen Meng Yi Su Distributed Parameter Identification Framework Based on Intelligent Algorithms for Permanent Magnet Synchronous Wind Generator Energies permanent magnet synchronous wind generator parameter identification distributed framework intelligent algorithms |
| title | Distributed Parameter Identification Framework Based on Intelligent Algorithms for Permanent Magnet Synchronous Wind Generator |
| title_full | Distributed Parameter Identification Framework Based on Intelligent Algorithms for Permanent Magnet Synchronous Wind Generator |
| title_fullStr | Distributed Parameter Identification Framework Based on Intelligent Algorithms for Permanent Magnet Synchronous Wind Generator |
| title_full_unstemmed | Distributed Parameter Identification Framework Based on Intelligent Algorithms for Permanent Magnet Synchronous Wind Generator |
| title_short | Distributed Parameter Identification Framework Based on Intelligent Algorithms for Permanent Magnet Synchronous Wind Generator |
| title_sort | distributed parameter identification framework based on intelligent algorithms for permanent magnet synchronous wind generator |
| topic | permanent magnet synchronous wind generator parameter identification distributed framework intelligent algorithms |
| url | https://www.mdpi.com/1996-1073/18/3/683 |
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