Optimization of energy storage VSG Control strategy based on RBF neural networks
In response to the issue that traditional energy storage VSGs （virtual synchronous generators） cannot simultaneously possess good disturbance resistance and rapid dynamic response capabilities， a control strategy for energy storage VSGs is proposed， optimizing the dynamic synchronizer using RBF （rad...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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zhejiang electric power
2024-03-01
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| Series: | Zhejiang dianli |
| Subjects: | |
| Online Access: | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=a72bafe0-dc89-4915-97b6-76ca677ff28c |
| _version_ | 1797232534009937920 |
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| author | GUAN Minyuan YAO Ying WU Zhenbin MAN Jingbin WU Weiqiang |
| author_facet | GUAN Minyuan YAO Ying WU Zhenbin MAN Jingbin WU Weiqiang |
| author_sort | GUAN Minyuan |
| collection | DOAJ |
| description | In response to the issue that traditional energy storage VSGs （virtual synchronous generators） cannot simultaneously possess good disturbance resistance and rapid dynamic response capabilities， a control strategy for energy storage VSGs is proposed， optimizing the dynamic synchronizer using RBF （radial basis function） neural networks. First， a mathematical model for VSG is established， analyzing the impact of rotor inertia and damping coefficient configuration on VSG performance. This analysis reveals the conflicting relationship between parameter configuration and dynamic response versus system dynamic stability. Subsequently， the transient unbalanced power of the rotor is taken as input for a three-layer forward structure RBF neural network algorithm. Through online learning with the RBF neural network algorithm， the optimal transient compensation power is obtained to dynamically adjust the input power of VSG， thereby reducing unbalanced rotor torque and enhancing the transient stability of VSG. Finally， simulation and comparative experiments are conducted to validate the effectiveness of the proposed control strategy. |
| first_indexed | 2024-04-24T16:01:48Z |
| format | Article |
| id | doaj.art-8555b0a1046e4733b9e73e5e77a9b5cb |
| institution | Directory Open Access Journal |
| issn | 1007-1881 |
| language | zho |
| last_indexed | 2024-04-24T16:01:48Z |
| publishDate | 2024-03-01 |
| publisher | zhejiang electric power |
| record_format | Article |
| series | Zhejiang dianli |
| spelling | doaj.art-8555b0a1046e4733b9e73e5e77a9b5cb2024-04-01T07:39:02Zzhozhejiang electric powerZhejiang dianli1007-18812024-03-01433556410.19585/j.zjdl.2024030071007-1881（2024）03-0055-10Optimization of energy storage VSG Control strategy based on RBF neural networksGUAN Minyuan0YAO Ying1WU Zhenbin2MAN Jingbin3WU Weiqiang4State Grid Huzhou Power Supply Company, Huzhou, Zhejiang 313000, ChinaState Grid Huzhou Power Supply Company, Huzhou, Zhejiang 313000, ChinaCollege of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, ChinaState Grid Huzhou Power Supply Company, Huzhou, Zhejiang 313000, ChinaState Grid Changxing Power Supply Company, Huzhou, Zhejiang 313100, ChinaIn response to the issue that traditional energy storage VSGs （virtual synchronous generators） cannot simultaneously possess good disturbance resistance and rapid dynamic response capabilities， a control strategy for energy storage VSGs is proposed， optimizing the dynamic synchronizer using RBF （radial basis function） neural networks. First， a mathematical model for VSG is established， analyzing the impact of rotor inertia and damping coefficient configuration on VSG performance. This analysis reveals the conflicting relationship between parameter configuration and dynamic response versus system dynamic stability. Subsequently， the transient unbalanced power of the rotor is taken as input for a three-layer forward structure RBF neural network algorithm. Through online learning with the RBF neural network algorithm， the optimal transient compensation power is obtained to dynamically adjust the input power of VSG， thereby reducing unbalanced rotor torque and enhancing the transient stability of VSG. Finally， simulation and comparative experiments are conducted to validate the effectiveness of the proposed control strategy.https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=a72bafe0-dc89-4915-97b6-76ca677ff28cvirtual synchronous generator controlrbf neural networkdynamic synchronizer controlenergy storage invertertransient stability |
| spellingShingle | GUAN Minyuan YAO Ying WU Zhenbin MAN Jingbin WU Weiqiang Optimization of energy storage VSG Control strategy based on RBF neural networks Zhejiang dianli virtual synchronous generator control rbf neural network dynamic synchronizer control energy storage inverter transient stability |
| title | Optimization of energy storage VSG Control strategy based on RBF neural networks |
| title_full | Optimization of energy storage VSG Control strategy based on RBF neural networks |
| title_fullStr | Optimization of energy storage VSG Control strategy based on RBF neural networks |
| title_full_unstemmed | Optimization of energy storage VSG Control strategy based on RBF neural networks |
| title_short | Optimization of energy storage VSG Control strategy based on RBF neural networks |
| title_sort | optimization of energy storage vsg control strategy based on rbf neural networks |
| topic | virtual synchronous generator control rbf neural network dynamic synchronizer control energy storage inverter transient stability |
| url | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=a72bafe0-dc89-4915-97b6-76ca677ff28c |
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