Equivalent method for DFIG wind farms based on modified LightGBM considering voltage deep drop faults
To address the challenges of inadequate accuracy in identifying the Crowbar action state and incomplete consideration of operating scenarios in existing methods for Doubly Fed Induction Generator (DFIG) wind farms, a DFIG wind farm equivalent method based on modified Light Gradient Boosting Machine...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S014206152500002X |
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| _version_ | 1832595392885161984 |
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| author | Xuecheng Liu Peixiao Fan Jun Yang Song Ke Binyu Ma Yangzhou Pei Jian Xu |
| author_facet | Xuecheng Liu Peixiao Fan Jun Yang Song Ke Binyu Ma Yangzhou Pei Jian Xu |
| author_sort | Xuecheng Liu |
| collection | DOAJ |
| description | To address the challenges of inadequate accuracy in identifying the Crowbar action state and incomplete consideration of operating scenarios in existing methods for Doubly Fed Induction Generator (DFIG) wind farms, a DFIG wind farm equivalent method based on modified Light Gradient Boosting Machine (mLightGBM) considering voltage deep drop faults is proposed. First, the low voltage ride through process of wind turbines is analysed, with particular consideration given to the scenario of partial wind turbines tripping off due to voltage deep drop faults in the wind farm. The factors influencing the Crowbar action state and trip-off state of wind turbines are identified, and a feature vector for wind turbine operating states is constructed. Second, based on simulations to obtain sample data of wind turbine operating states under different operating scenarios in wind farms, a classification model based on mLightGBM is established. Different weights are assigned to various samples and hyperparameter optimization is conducted to enhance the model’s classification accuracy. Finally, a two-stage clustering method driven by a data-model hybrid approach is proposed. Under specific operating conditions, wind turbine clusters are divided sequentially into two stages based on the mLightGBM classification results and the wind speed range of the turbines. The final equivalent model is derived through parameter calculations. Simulation results demonstrate that the proposed DFIG wind farms equivalent model, compared to traditional methods, not only can adapt to a broader range of operating scenarios but also achieves superior identification of wind turbine operating states, making the equivalent method rational and effective. |
| format | Article |
| id | doaj-art-4c8402ba184d46a29a2321432ba1cc95 |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-4c8402ba184d46a29a2321432ba1cc952025-01-19T06:24:05ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-03-01164110451Equivalent method for DFIG wind farms based on modified LightGBM considering voltage deep drop faultsXuecheng Liu0Peixiao Fan1Jun Yang2Song Ke3Binyu Ma4Yangzhou Pei5Jian Xu6Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan University Wuhan China; School of Electrical Engineering and Automation Wuhan University Wuhan ChinaHubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan University Wuhan China; School of Electrical Engineering and Automation Wuhan University Wuhan ChinaHubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan University Wuhan China; School of Electrical Engineering and Automation Wuhan University Wuhan China; Corresponding author at: Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, School of Electrical Engineering and Automation, Wuhan University, Wuhan, China.Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan University Wuhan China; School of Electrical Engineering and Automation Wuhan University Wuhan ChinaHubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan University Wuhan China; School of Electrical Engineering and Automation Wuhan University Wuhan ChinaHubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan University Wuhan China; School of Electrical Engineering and Automation Wuhan University Wuhan ChinaHubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation Wuhan University Wuhan China; School of Electrical Engineering and Automation Wuhan University Wuhan ChinaTo address the challenges of inadequate accuracy in identifying the Crowbar action state and incomplete consideration of operating scenarios in existing methods for Doubly Fed Induction Generator (DFIG) wind farms, a DFIG wind farm equivalent method based on modified Light Gradient Boosting Machine (mLightGBM) considering voltage deep drop faults is proposed. First, the low voltage ride through process of wind turbines is analysed, with particular consideration given to the scenario of partial wind turbines tripping off due to voltage deep drop faults in the wind farm. The factors influencing the Crowbar action state and trip-off state of wind turbines are identified, and a feature vector for wind turbine operating states is constructed. Second, based on simulations to obtain sample data of wind turbine operating states under different operating scenarios in wind farms, a classification model based on mLightGBM is established. Different weights are assigned to various samples and hyperparameter optimization is conducted to enhance the model’s classification accuracy. Finally, a two-stage clustering method driven by a data-model hybrid approach is proposed. Under specific operating conditions, wind turbine clusters are divided sequentially into two stages based on the mLightGBM classification results and the wind speed range of the turbines. The final equivalent model is derived through parameter calculations. Simulation results demonstrate that the proposed DFIG wind farms equivalent model, compared to traditional methods, not only can adapt to a broader range of operating scenarios but also achieves superior identification of wind turbine operating states, making the equivalent method rational and effective.http://www.sciencedirect.com/science/article/pii/S014206152500002XDFIG wind farmsLow voltage ride throughTrip offCrowbarModified LightGBMEquivalent model |
| spellingShingle | Xuecheng Liu Peixiao Fan Jun Yang Song Ke Binyu Ma Yangzhou Pei Jian Xu Equivalent method for DFIG wind farms based on modified LightGBM considering voltage deep drop faults International Journal of Electrical Power & Energy Systems DFIG wind farms Low voltage ride through Trip off Crowbar Modified LightGBM Equivalent model |
| title | Equivalent method for DFIG wind farms based on modified LightGBM considering voltage deep drop faults |
| title_full | Equivalent method for DFIG wind farms based on modified LightGBM considering voltage deep drop faults |
| title_fullStr | Equivalent method for DFIG wind farms based on modified LightGBM considering voltage deep drop faults |
| title_full_unstemmed | Equivalent method for DFIG wind farms based on modified LightGBM considering voltage deep drop faults |
| title_short | Equivalent method for DFIG wind farms based on modified LightGBM considering voltage deep drop faults |
| title_sort | equivalent method for dfig wind farms based on modified lightgbm considering voltage deep drop faults |
| topic | DFIG wind farms Low voltage ride through Trip off Crowbar Modified LightGBM Equivalent model |
| url | http://www.sciencedirect.com/science/article/pii/S014206152500002X |
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