LVRT Measurement Model and Transient Parameter Identification of Wind Turbine Based on Chaotic Particle Swarm
The high-accuracy simulation model is the basis for transient stability analysis of large-scale wind power integration. However, the control strategies and parameters of doubly-fed wind turbines are technical secrets that are difficult to obtain, and the accuracy of model simulation is difficult to...
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State Grid Energy Research Institute
2024-08-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202310040 |
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| author | Dan LI Shiyao QIN Shaolin LI Jing HE |
| author_facet | Dan LI Shiyao QIN Shaolin LI Jing HE |
| author_sort | Dan LI |
| collection | DOAJ |
| description | The high-accuracy simulation model is the basis for transient stability analysis of large-scale wind power integration. However, the control strategies and parameters of doubly-fed wind turbines are technical secrets that are difficult to obtain, and the accuracy of model simulation is difficult to guarantee. In order to address the fault transient modeling problems of doubly-fed wind turbines, a measured data-based modeling and parameter identification method of doubly-fed wind turbines is proposed. Firstly, based on the DFIG model and control structure of the Power System Integrated Stability Program (PSASP), a low voltage ride through (LVRT) control mathematical model is established to analyze the fault transient process, and the LVRT transient control core parameters are clarified. Secondly, based on part of the field measured LVRT data of doubly-fed wind turbines, the fault transient parameters are identified with the chaotic particle swarm optimization algorithm. Finally, the accuracy of the identification parameters are analyzed and verified based on the remaining measured data. The simulation results have verified the effectiveness and accuracy of the proposed parameter identification method. The proposed method has strong generalization ability and high accuracy of identification results, and is of great engineering application value. |
| format | Article |
| id | doaj-art-059376839dbc47d184b2c94c210777f6 |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2024-08-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-059376839dbc47d184b2c94c210777f62025-08-20T02:56:45ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492024-08-01578758410.11930/j.issn.1004-9649.202310040zgdl-57-7-lidanLVRT Measurement Model and Transient Parameter Identification of Wind Turbine Based on Chaotic Particle SwarmDan LI0Shiyao QIN1Shaolin LI2Jing HE3National Key Laboratory of Renewable Energy Grid-Intergration (China Electric Power Research Institute), Beijing 100192, ChinaNational Key Laboratory of Renewable Energy Grid-Intergration (China Electric Power Research Institute), Beijing 100192, ChinaNational Key Laboratory of Renewable Energy Grid-Intergration (China Electric Power Research Institute), Beijing 100192, ChinaNational Key Laboratory of Renewable Energy Grid-Intergration (China Electric Power Research Institute), Beijing 100192, ChinaThe high-accuracy simulation model is the basis for transient stability analysis of large-scale wind power integration. However, the control strategies and parameters of doubly-fed wind turbines are technical secrets that are difficult to obtain, and the accuracy of model simulation is difficult to guarantee. In order to address the fault transient modeling problems of doubly-fed wind turbines, a measured data-based modeling and parameter identification method of doubly-fed wind turbines is proposed. Firstly, based on the DFIG model and control structure of the Power System Integrated Stability Program (PSASP), a low voltage ride through (LVRT) control mathematical model is established to analyze the fault transient process, and the LVRT transient control core parameters are clarified. Secondly, based on part of the field measured LVRT data of doubly-fed wind turbines, the fault transient parameters are identified with the chaotic particle swarm optimization algorithm. Finally, the accuracy of the identification parameters are analyzed and verified based on the remaining measured data. The simulation results have verified the effectiveness and accuracy of the proposed parameter identification method. The proposed method has strong generalization ability and high accuracy of identification results, and is of great engineering application value.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202310040double-fed induction generatorlow voltage ride throughparameter identificationmeasured datachaotic particle swarm |
| spellingShingle | Dan LI Shiyao QIN Shaolin LI Jing HE LVRT Measurement Model and Transient Parameter Identification of Wind Turbine Based on Chaotic Particle Swarm Zhongguo dianli double-fed induction generator low voltage ride through parameter identification measured data chaotic particle swarm |
| title | LVRT Measurement Model and Transient Parameter Identification of Wind Turbine Based on Chaotic Particle Swarm |
| title_full | LVRT Measurement Model and Transient Parameter Identification of Wind Turbine Based on Chaotic Particle Swarm |
| title_fullStr | LVRT Measurement Model and Transient Parameter Identification of Wind Turbine Based on Chaotic Particle Swarm |
| title_full_unstemmed | LVRT Measurement Model and Transient Parameter Identification of Wind Turbine Based on Chaotic Particle Swarm |
| title_short | LVRT Measurement Model and Transient Parameter Identification of Wind Turbine Based on Chaotic Particle Swarm |
| title_sort | lvrt measurement model and transient parameter identification of wind turbine based on chaotic particle swarm |
| topic | double-fed induction generator low voltage ride through parameter identification measured data chaotic particle swarm |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202310040 |
| work_keys_str_mv | AT danli lvrtmeasurementmodelandtransientparameteridentificationofwindturbinebasedonchaoticparticleswarm AT shiyaoqin lvrtmeasurementmodelandtransientparameteridentificationofwindturbinebasedonchaoticparticleswarm AT shaolinli lvrtmeasurementmodelandtransientparameteridentificationofwindturbinebasedonchaoticparticleswarm AT jinghe lvrtmeasurementmodelandtransientparameteridentificationofwindturbinebasedonchaoticparticleswarm |