LAPSE:2023.13147
Published Article
LAPSE:2023.13147
Disturbance Observer-Based Model Predictive Super-Twisting Control for Soft Open Point
February 28, 2023
Abstract
This paper presents a disturbance observer-based model predictive of super-twisting control for Soft Open Point (SOP). First, with the consideration of the disturbances caused by parameter mismatches and unmodelled dynamics, a super-twisting sliding-mode observer (STO) is proposed to observe the disturbances, and the observed disturbances are introduced into the inner-loop as the compensation to improve the anti-disturbance of SOP system. Second, the outer-loop controller is designed by applying the super-twisting sliding-mode control (STC) approach to improve the dynamic performance and robustness. Third, to deal with large current harmonics by traditional model predictive control (MPC), a Three-Vector-based MPC (TV-MPC) is proposed to increase the number of voltage vectors in a sampling time. Finally, it is verified by simulations that the proposed method can reduce current harmonics, DC-side voltage setting time and improve the dynamic performance of SOP system effectively. In case of parameter mismatches, the proposed observer can observe the disturbances correctly to enhance the robustness of the SOP system.
Keywords
model predictive control (MPC), parameter mismatch, sliding-mode observer, Soft Open Point (SOP), super-twisting algorithm
Suggested Citation
Wang Z, Zhou H, Su H. Disturbance Observer-Based Model Predictive Super-Twisting Control for Soft Open Point. (2023). LAPSE:2023.13147
Author Affiliations
Wang Z: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China [ORCID]
Zhou H: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China [ORCID]
Su H: School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150001, China
Journal Name
Energies
Volume
15
Issue
10
First Page
3657
Year
2022
Publication Date
2022-05-16
ISSN
1996-1073
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Original Submission
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PII: en15103657, Publication Type: Journal Article
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LAPSE:2023.13147
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https://doi.org/10.3390/en15103657
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