Two-Step Mpc Vector-Screening Prediction Method Based on Vsg
With the development of new energy technologies and the large-scale grid connection of grid-connected inverters, the performance and stability of inverters in response to grid frequency and voltage disturbances have received increasing attention. This paper proposes a two-step voltage sector-divided vector-screening predictive method for three-level LC-type grid-tied inverters based on Virtual Synchronous GeneratorModel Predictive Control (VSG-MPC) technology. This method utilizes damping and inertia characteristics to effectively handle grid frequency and voltage disturbances. At the same time, based on single-step prediction, the vector screening is further improved. By dividing the sectors into large and small ones, the 27 candidate predictive vectors are reduced to 4 to 5 vectors for two-step prediction, which greatly improves the prediction speed and solves the delay problem existing in single-step prediction. It is of great significance for maintaining the stability of the output voltage. Simulation results show that the proposed method has reliable output and anti-interference capabilities, verifying the correctness and effectiveness of the method.
Authors
- José Rodríguez (ORCID: https://orcid.org/0000-0002-1410-4121)
- Cristian García (ORCID: https://orcid.org/0000-0002-7939-422X)
- Mingdi Fan (ORCID: https://orcid.org/0000-0003-3976-8204)
- Haoran Feng (ORCID: https://orcid.org/0009-0001-0239-9137)
- Wei Li (ORCID: https://orcid.org/0000-0003-4242-1615)
- Yong Yang (ORCID: https://orcid.org/0000-0002-7825-0494)
- Junhao Lu
- H. Peng
- Qingwei Geng
- Yong Tang
Institutions
- University of Talca (CL)
- San Sebastián University (CL)
- Soochow University (CN)
- China Electric Equipment Group (China) (CN)
Publication Details
- Published
- 2025-06-05
- DOI
- https://doi.org/10.1109/precede63178.2025.11131042
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China