Data-Driven Hybrid Equivalent Dynamic Modeling of Multiple Photovoltaic Power Stations Based on Ensemble Gated Recurrent Unit
With the increasing penetration of the photovoltaic (PV) in the distributed grid network, the dynamic response analysis of the system becomes more and more complex and costs lots of computational time in the simulation. To cut down the computational resources while guaranteeing the accuracy, this pa...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-07-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fenrg.2020.00185/full |
| _version_ | 1819019060100202496 |
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| author | Huan Long Huan Long Shaohui Xu Xiao Lu Zijun Yang Chen Li Jiangping Jing Zhi Wu |
| author_facet | Huan Long Huan Long Shaohui Xu Xiao Lu Zijun Yang Chen Li Jiangping Jing Zhi Wu |
| author_sort | Huan Long |
| collection | DOAJ |
| description | With the increasing penetration of the photovoltaic (PV) in the distributed grid network, the dynamic response analysis of the system becomes more and more complex and costs lots of computational time in the simulation. To cut down the computational resources while guaranteeing the accuracy, this paper proposes a data-driven hybrid equivalent model for the dynamic response process of the multiple PV power stations. The data-driven hybrid equivalent model contains the simple equivalent model and data-driven error correction model. In the equivalent model, the distributed PV power stations in the same branch are equivalent to one power station model based on the parameter equivalence and feeder equivalence. The data-driven error correction model tracks and corrects the difference of dynamic response between the equivalent model and precise model. The ensemble Gated Recurrent Unit (GRU) model based on the bagging ensemble structure utilizes the simple equivalent dynamic response as input to learn the dynamic response errors. The simulation results validate the super-performance of the proposed model both in the response speed and accuracy. |
| first_indexed | 2024-12-21T03:29:18Z |
| format | Article |
| id | doaj.art-5818dbb1a1f943e3afe4f119350ca216 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-12-21T03:29:18Z |
| publishDate | 2020-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-5818dbb1a1f943e3afe4f119350ca2162022-12-21T19:17:30ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2020-07-01810.3389/fenrg.2020.00185566191Data-Driven Hybrid Equivalent Dynamic Modeling of Multiple Photovoltaic Power Stations Based on Ensemble Gated Recurrent UnitHuan Long0Huan Long1Shaohui Xu2Xiao Lu3Zijun Yang4Chen Li5Jiangping Jing6Zhi Wu7School of Electric Engineering, Southeast University, Nanjing, ChinaJiangsu Key Laboratory of Smart Grid Technology and Equipment, Nanjing, ChinaSchool of Electric Engineering, Southeast University, Nanjing, ChinaState Grid Jiangsu Electric Power Company Limited, Nanjing, ChinaState Grid Jiangsu Electric Power Company Limited, Nanjing, ChinaNational Electric Power Dispatching and Communication Center, Nanjing, ChinaState Grid Jiangsu Electric Power Company Limited, Nanjing, ChinaSchool of Electric Engineering, Southeast University, Nanjing, ChinaWith the increasing penetration of the photovoltaic (PV) in the distributed grid network, the dynamic response analysis of the system becomes more and more complex and costs lots of computational time in the simulation. To cut down the computational resources while guaranteeing the accuracy, this paper proposes a data-driven hybrid equivalent model for the dynamic response process of the multiple PV power stations. The data-driven hybrid equivalent model contains the simple equivalent model and data-driven error correction model. In the equivalent model, the distributed PV power stations in the same branch are equivalent to one power station model based on the parameter equivalence and feeder equivalence. The data-driven error correction model tracks and corrects the difference of dynamic response between the equivalent model and precise model. The ensemble Gated Recurrent Unit (GRU) model based on the bagging ensemble structure utilizes the simple equivalent dynamic response as input to learn the dynamic response errors. The simulation results validate the super-performance of the proposed model both in the response speed and accuracy.https://www.frontiersin.org/article/10.3389/fenrg.2020.00185/fullcentral PV power stationdistributed PV power stationdata-driven dynamic modelingequivalent modelgated recurrent unit |
| spellingShingle | Huan Long Huan Long Shaohui Xu Xiao Lu Zijun Yang Chen Li Jiangping Jing Zhi Wu Data-Driven Hybrid Equivalent Dynamic Modeling of Multiple Photovoltaic Power Stations Based on Ensemble Gated Recurrent Unit Frontiers in Energy Research central PV power station distributed PV power station data-driven dynamic modeling equivalent model gated recurrent unit |
| title | Data-Driven Hybrid Equivalent Dynamic Modeling of Multiple Photovoltaic Power Stations Based on Ensemble Gated Recurrent Unit |
| title_full | Data-Driven Hybrid Equivalent Dynamic Modeling of Multiple Photovoltaic Power Stations Based on Ensemble Gated Recurrent Unit |
| title_fullStr | Data-Driven Hybrid Equivalent Dynamic Modeling of Multiple Photovoltaic Power Stations Based on Ensemble Gated Recurrent Unit |
| title_full_unstemmed | Data-Driven Hybrid Equivalent Dynamic Modeling of Multiple Photovoltaic Power Stations Based on Ensemble Gated Recurrent Unit |
| title_short | Data-Driven Hybrid Equivalent Dynamic Modeling of Multiple Photovoltaic Power Stations Based on Ensemble Gated Recurrent Unit |
| title_sort | data driven hybrid equivalent dynamic modeling of multiple photovoltaic power stations based on ensemble gated recurrent unit |
| topic | central PV power station distributed PV power station data-driven dynamic modeling equivalent model gated recurrent unit |
| url | https://www.frontiersin.org/article/10.3389/fenrg.2020.00185/full |
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