Adaptive Virtual Synchronous Generator Based on Model Predictive Control with Improved Frequency Stability
With the massive integration of renewable energy into the grid, grid inertia and its stability continue to decrease. To improve inertia and facilitate grid restoration, a control strategy for radial basis function virtual synchronous generators based on model predictive control (MPC-VSG-RBF) is prop...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/22/8385 |
| _version_ | 1797465448487321600 |
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| author | Xuhong Yang Hui Li Wei Jia Zhongxin Liu Yu Pan Fengwei Qian |
| author_facet | Xuhong Yang Hui Li Wei Jia Zhongxin Liu Yu Pan Fengwei Qian |
| author_sort | Xuhong Yang |
| collection | DOAJ |
| description | With the massive integration of renewable energy into the grid, grid inertia and its stability continue to decrease. To improve inertia and facilitate grid restoration, a control strategy for radial basis function virtual synchronous generators based on model predictive control (MPC-VSG-RBF) is proposed in this paper. In this method, virtual synchronous generator (VSG) control strategy is introduced into the model predictive control (MPC), so that the reference value of the inner loop current can vary with the grid voltage and frequency. Using the radial basis function (RBF) neural network to adjust the VSG virtual inertia online can solve the large fluctuation of frequency and power caused by excessive load fluctuation. The simulation model was built based on MATLAB and compared and analyzed with the MPC control method. The simulation results show that: when the output power of the inverter changes, the model predictive control of the adaptive virtual synchronous generator can increase the inertia and stability of the power grid; by adjusting the moment of inertia, the system damping ratio is improved to effectively suppress the transient process overshoot and oscillation in medium power. |
| first_indexed | 2024-03-09T18:22:34Z |
| format | Article |
| id | doaj.art-86ee80f85a1e44d7aab7fd39b921e0c9 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T18:22:34Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-86ee80f85a1e44d7aab7fd39b921e0c92023-11-24T08:12:16ZengMDPI AGEnergies1996-10732022-11-011522838510.3390/en15228385Adaptive Virtual Synchronous Generator Based on Model Predictive Control with Improved Frequency StabilityXuhong Yang0Hui Li1Wei Jia2Zhongxin Liu3Yu Pan4Fengwei Qian5College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, ChinaCollege of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, ChinaShanghai Institute of Space Power-Sources/State Key Laboratory of Space Power-Sources Technology, Shanghai 200245, ChinaShanghai Institute of Space Power-Sources/State Key Laboratory of Space Power-Sources Technology, Shanghai 200245, ChinaShanghai Institute of Space Power-Sources/State Key Laboratory of Space Power-Sources Technology, Shanghai 200245, ChinaShanghai Solar Energy Engineering Technology Research Center, Shanghai 200245, ChinaWith the massive integration of renewable energy into the grid, grid inertia and its stability continue to decrease. To improve inertia and facilitate grid restoration, a control strategy for radial basis function virtual synchronous generators based on model predictive control (MPC-VSG-RBF) is proposed in this paper. In this method, virtual synchronous generator (VSG) control strategy is introduced into the model predictive control (MPC), so that the reference value of the inner loop current can vary with the grid voltage and frequency. Using the radial basis function (RBF) neural network to adjust the VSG virtual inertia online can solve the large fluctuation of frequency and power caused by excessive load fluctuation. The simulation model was built based on MATLAB and compared and analyzed with the MPC control method. The simulation results show that: when the output power of the inverter changes, the model predictive control of the adaptive virtual synchronous generator can increase the inertia and stability of the power grid; by adjusting the moment of inertia, the system damping ratio is improved to effectively suppress the transient process overshoot and oscillation in medium power.https://www.mdpi.com/1996-1073/15/22/8385distributed power generationmodel predictive controlradial basis function neural networkvirtual synchronous generator |
| spellingShingle | Xuhong Yang Hui Li Wei Jia Zhongxin Liu Yu Pan Fengwei Qian Adaptive Virtual Synchronous Generator Based on Model Predictive Control with Improved Frequency Stability Energies distributed power generation model predictive control radial basis function neural network virtual synchronous generator |
| title | Adaptive Virtual Synchronous Generator Based on Model Predictive Control with Improved Frequency Stability |
| title_full | Adaptive Virtual Synchronous Generator Based on Model Predictive Control with Improved Frequency Stability |
| title_fullStr | Adaptive Virtual Synchronous Generator Based on Model Predictive Control with Improved Frequency Stability |
| title_full_unstemmed | Adaptive Virtual Synchronous Generator Based on Model Predictive Control with Improved Frequency Stability |
| title_short | Adaptive Virtual Synchronous Generator Based on Model Predictive Control with Improved Frequency Stability |
| title_sort | adaptive virtual synchronous generator based on model predictive control with improved frequency stability |
| topic | distributed power generation model predictive control radial basis function neural network virtual synchronous generator |
| url | https://www.mdpi.com/1996-1073/15/22/8385 |
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