Data-driven weight factor design method, model predictive control method and device
By using a data-driven weighting factor design method, the weighting factor of the midpoint voltage balance term of the three-level converter is optimized, which solves the problem of cumbersome adjustment in the traditional method, realizes capacitor voltage balance and current stability, and improves the control effect.
CN119906288BActive Publication Date: 2026-06-05CHAJNA MAJNING DRAJVS EHND AUTOMEHJSHN KO
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHAJNA MAJNING DRAJVS EHND AUTOMEHJSHN KO
- Filing Date
- 2024-12-30
- Publication Date
- 2026-06-05
AI Technical Summary
Technical Problem
Traditional model-based predictive control methods involve cumbersome and theoretically unfounded adjustments to weighting factors, resulting in low flexibility.
Method used
A data-driven weighting factor design method is adopted. By calculating the average value and nonlinear form of the absolute value of the midpoint voltage online, and combining it with model-free adaptive control theory, the cost function of pseudo-partial derivatives and midpoint voltage balance terms is set to optimize the weighting factor.
Benefits of technology
It achieves balanced and stable operation of the capacitor voltage of the three-level converter, reduces current ripple and voltage fluctuation, and improves control flexibility and efficiency.
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Smart Images
Figure CN119906288B_ABST
Abstract
The application discloses a weight factor design method based on data driving, a model predictive control method and device. The model predictive control method comprises the following steps: collecting state variables of a T-type three-level energy storage converter at k time; establishing a discrete mathematical model of the T-type three-level energy storage converter in a two-phase static alpha-beta coordinate system, and obtaining a reference value of a grid current at k+1 time according to a Lagrange extrapolation theorem; online optimizing a weight factor of a midpoint voltage balance term; determining a candidate voltage vector based on a voltage vector conversion rule; calculating a cost function value of each candidate voltage vector through a cost function, and selecting a voltage vector corresponding to a minimum cost function value as an optimal voltage vector of a kth control period; and applying the optimal voltage vector and a gate drive signal corresponding to an action time of the optimal voltage vector to power electronic semiconductor devices of the T-type three-level energy storage converter. The application can online optimize the weight factor, and greatly simplifies a design process of the weight factor.
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