An improved model-free prediction control strategy based on ultra-local model for three-level T-type grid-tied converter

Nan Jin; Huan Xie; Zhiwen Hou; Jie Wu

doi:10.1117/12.2639896

27 June 2022 An improved model-free prediction control strategy based on ultra-local model for three-level T-type grid-tied converter

Nan Jin, Huan Xie, Zhiwen Hou, Jie Wu

Author Affiliations +

Proceedings Volume 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022); 1225304 (2022) https://doi.org/10.1117/12.2639896
Event: Second International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 2022, Qingdao, China

Abstract

Three-level T-type grid-tied converter is the important device to connect renewable energy and grid. When the inductance parameters in converter model mismatch with those in controller, the conventional model predictive control (MPC) strategy causes grid-tied distortion and current errors. To solve the problem, an improved model-free prediction control strategy using Runge-Kutta algorithm (RKA) for three-level T-type grid-tied converter is proposed. The model-free predictive control strategy is analyzed based on ultra-local model of converter system. In order to calculate the lumped disturbance of the system, RKA is used in ultra-local model. Because the derived function that calculates parameters in RKA are unknown, Lagrange interpolation is performed to calculate the parameters. The ultra-local model of the converter system can be obtained by RKA and Lagrange algorithm. Compared with the conventional MPC strategy, the model-free current prediction control strategy based on RKA improves parameter robustness. Finally, simulation results verify the effectiveness of the proposed approach.

Citation Download Citation

Nan Jin, Huan Xie, Zhiwen Hou, and Jie Wu "An improved model-free prediction control strategy based on ultra-local model for three-level T-type grid-tied converter", Proc. SPIE 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 1225304 (27 June 2022); https://doi.org/10.1117/12.2639896

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