Dynamic Equivalent Modeling for Wind Farms With DFIGs Using the Artificial Bee Colony With K-Means Algorithm
With the increasing penetration of wind power, it is recognized that wind power will have a greater and greater impact on the planning and operation of the original power system. And the detailed modeling of wind farm with doubly-fed induction wind generator (DFIG) will require large storage and com...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9197650/ |
| _version_ | 1818727451421835264 |
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| author | Xiaohui Wang Hao Yu Yong Lin Zhemeng Zhang Xianfu Gong |
| author_facet | Xiaohui Wang Hao Yu Yong Lin Zhemeng Zhang Xianfu Gong |
| author_sort | Xiaohui Wang |
| collection | DOAJ |
| description | With the increasing penetration of wind power, it is recognized that wind power will have a greater and greater impact on the planning and operation of the original power system. And the detailed modeling of wind farm with doubly-fed induction wind generator (DFIG) will require large storage and computation resources, which poses technical challenges for equivalent modeling of wind farm. In this paper, a multi-machine dynamic equivalent modeling method for wind farms with DFIGs is proposed. First, the artificial bee colony with k-means (ABC-KM) algorithm is proposed to improve the effectiveness of wind farm clustering. Second, the operating data composed of wind speed, pitch angle, rotor angular velocity, rotor current, real-time active and reactive power are selected as clustering indicators. A wind farm with DFIGs is divided into several groups and DFIGs in the same group are clustered as one DFIG through equivalent parameter aggregation. The proposed wind farm modeling method consisting of clustering method and clustering indicators is verified by comparing the simulation results of equivalent and detailed models at steady-state and dynamic-state cases. |
| first_indexed | 2024-12-17T22:14:18Z |
| format | Article |
| id | doaj.art-424186451fa347bebaf90b02ff887343 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T22:14:18Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-424186451fa347bebaf90b02ff8873432022-12-21T21:30:39ZengIEEEIEEE Access2169-35362020-01-01817372317373110.1109/ACCESS.2020.30242129197650Dynamic Equivalent Modeling for Wind Farms With DFIGs Using the Artificial Bee Colony With K-Means AlgorithmXiaohui Wang0https://orcid.org/0000-0001-8142-9041Hao Yu1https://orcid.org/0000-0002-5108-7682Yong Lin2Zhemeng Zhang3Xianfu Gong4College of Electrical and Information Engineering, Hunan University, Changsha, ChinaGrid Planning and Research Center, Guangdong Power Grid Company, Ltd., Guangzhou, ChinaGrid Planning and Research Center, Guangdong Power Grid Company, Ltd., Guangzhou, ChinaCollege of Electrical and Information Engineering, Hunan University, Changsha, ChinaGrid Planning and Research Center, Guangdong Power Grid Company, Ltd., Guangzhou, ChinaWith the increasing penetration of wind power, it is recognized that wind power will have a greater and greater impact on the planning and operation of the original power system. And the detailed modeling of wind farm with doubly-fed induction wind generator (DFIG) will require large storage and computation resources, which poses technical challenges for equivalent modeling of wind farm. In this paper, a multi-machine dynamic equivalent modeling method for wind farms with DFIGs is proposed. First, the artificial bee colony with k-means (ABC-KM) algorithm is proposed to improve the effectiveness of wind farm clustering. Second, the operating data composed of wind speed, pitch angle, rotor angular velocity, rotor current, real-time active and reactive power are selected as clustering indicators. A wind farm with DFIGs is divided into several groups and DFIGs in the same group are clustered as one DFIG through equivalent parameter aggregation. The proposed wind farm modeling method consisting of clustering method and clustering indicators is verified by comparing the simulation results of equivalent and detailed models at steady-state and dynamic-state cases.https://ieeexplore.ieee.org/document/9197650/Dynamic equivalent modelwind farm with DFIGclustering indicatorsABC-KM clustering algorithmactive prosumers |
| spellingShingle | Xiaohui Wang Hao Yu Yong Lin Zhemeng Zhang Xianfu Gong Dynamic Equivalent Modeling for Wind Farms With DFIGs Using the Artificial Bee Colony With K-Means Algorithm IEEE Access Dynamic equivalent model wind farm with DFIG clustering indicators ABC-KM clustering algorithm active prosumers |
| title | Dynamic Equivalent Modeling for Wind Farms With DFIGs Using the Artificial Bee Colony With K-Means Algorithm |
| title_full | Dynamic Equivalent Modeling for Wind Farms With DFIGs Using the Artificial Bee Colony With K-Means Algorithm |
| title_fullStr | Dynamic Equivalent Modeling for Wind Farms With DFIGs Using the Artificial Bee Colony With K-Means Algorithm |
| title_full_unstemmed | Dynamic Equivalent Modeling for Wind Farms With DFIGs Using the Artificial Bee Colony With K-Means Algorithm |
| title_short | Dynamic Equivalent Modeling for Wind Farms With DFIGs Using the Artificial Bee Colony With K-Means Algorithm |
| title_sort | dynamic equivalent modeling for wind farms with dfigs using the artificial bee colony with k means algorithm |
| topic | Dynamic equivalent model wind farm with DFIG clustering indicators ABC-KM clustering algorithm active prosumers |
| url | https://ieeexplore.ieee.org/document/9197650/ |
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