Polynomial surface-fitting evaluation of new energy maximum power generation capacity based on random forest association analysis and support vector regression
Focusing on frequency problems caused by wind power integration in ultra-high-voltage DC systems, an accurate assessment of the maximum generation capacity of large-scale new energy sources can help determine the available frequency regulation capacity of new energy sources and improve the frequency...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-12-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1323559/full |
| _version_ | 1797384245315895296 |
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| author | Yuzhuo Hu Hui Li Yuan Zeng Qichao Chen Haosen Cao Wei Chen |
| author_facet | Yuzhuo Hu Hui Li Yuan Zeng Qichao Chen Haosen Cao Wei Chen |
| author_sort | Yuzhuo Hu |
| collection | DOAJ |
| description | Focusing on frequency problems caused by wind power integration in ultra-high-voltage DC systems, an accurate assessment of the maximum generation capacity of large-scale new energy sources can help determine the available frequency regulation capacity of new energy sources and improve the frequency stability control of power systems. First, a random forest model is constructed to analyze the key features and select the indexes significantly related to the generation capacity to form the input feature set. Second, by establishing an iterative construction model of the polynomial fitting surface, data are maximized by the upper envelope surface, and an effective sample set is constructed. Furthermore, a new energy maximum generation capacity assessment model adopts the support vector machine regression algorithm under the whale optimization algorithm to derive the correspondence between the input features and maximum generation capacity of new energy sources. Finally, we validate the applicability and effectiveness of the new maximum energy generation capacity evaluation model based on the results of an actual wind farm. |
| first_indexed | 2024-03-08T21:32:38Z |
| format | Article |
| id | doaj.art-d548f00847f14e759fe805bf29547c33 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-03-08T21:32:38Z |
| publishDate | 2023-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-d548f00847f14e759fe805bf29547c332023-12-21T04:56:49ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-12-011110.3389/fenrg.2023.13235591323559Polynomial surface-fitting evaluation of new energy maximum power generation capacity based on random forest association analysis and support vector regressionYuzhuo Hu0Hui Li1Yuan Zeng2Qichao Chen3Haosen Cao4Wei Chen5Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, ChinaState Grid Economic and Technological Research Institute Co., Ltd, Beijing, ChinaKey Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, ChinaState Grid Economic and Technological Research Institute Co., Ltd, Beijing, ChinaKey Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, ChinaState Grid Sichuan Economic Research Institute, Chengdu, ChinaFocusing on frequency problems caused by wind power integration in ultra-high-voltage DC systems, an accurate assessment of the maximum generation capacity of large-scale new energy sources can help determine the available frequency regulation capacity of new energy sources and improve the frequency stability control of power systems. First, a random forest model is constructed to analyze the key features and select the indexes significantly related to the generation capacity to form the input feature set. Second, by establishing an iterative construction model of the polynomial fitting surface, data are maximized by the upper envelope surface, and an effective sample set is constructed. Furthermore, a new energy maximum generation capacity assessment model adopts the support vector machine regression algorithm under the whale optimization algorithm to derive the correspondence between the input features and maximum generation capacity of new energy sources. Finally, we validate the applicability and effectiveness of the new maximum energy generation capacity evaluation model based on the results of an actual wind farm.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1323559/fullrandom forestfeature selectionpolynomial fitting surfaceswhale optimizationsupport vector regression machine |
| spellingShingle | Yuzhuo Hu Hui Li Yuan Zeng Qichao Chen Haosen Cao Wei Chen Polynomial surface-fitting evaluation of new energy maximum power generation capacity based on random forest association analysis and support vector regression Frontiers in Energy Research random forest feature selection polynomial fitting surfaces whale optimization support vector regression machine |
| title | Polynomial surface-fitting evaluation of new energy maximum power generation capacity based on random forest association analysis and support vector regression |
| title_full | Polynomial surface-fitting evaluation of new energy maximum power generation capacity based on random forest association analysis and support vector regression |
| title_fullStr | Polynomial surface-fitting evaluation of new energy maximum power generation capacity based on random forest association analysis and support vector regression |
| title_full_unstemmed | Polynomial surface-fitting evaluation of new energy maximum power generation capacity based on random forest association analysis and support vector regression |
| title_short | Polynomial surface-fitting evaluation of new energy maximum power generation capacity based on random forest association analysis and support vector regression |
| title_sort | polynomial surface fitting evaluation of new energy maximum power generation capacity based on random forest association analysis and support vector regression |
| topic | random forest feature selection polynomial fitting surfaces whale optimization support vector regression machine |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1323559/full |
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