A Generalized Analytical Tuning Approach for Model Predictive Controlled Grid-Tied Converters Under Wide Range of Grid Inductance Variation
Bibliographic record
Abstract
In medium-high power applications, the LCL is the preferred filter topology to attain efficient power conversion. The filter provides a high damping attenuation of -60 dB/decade above the resonance frequency at the cost of a higher order plant, complex parameter design, and, increased vulnerability to un-modeled disturbances. Recently, model predictive control applied to power electronic converters has experienced great interest from researchers. The technique is tailored to the control of complex MIMO systems such as the LCL filter as it allows simultaneous regulation of several state-variables via a user defined cost function. However, the tuning of weighting factors (WFs) within the cost function is not trivial, and in most cases employs an empirical procedure. This paper presents an analytical procedure for tuning of WFs for indirect model predictive current controlled grid-tied converters. The method is based on using analytical closed-form expressions, that relate closed-loop poles of the filter with WFs via physical plant parameters. The presented expressions generalize the tuning of WFs to any arbitrary LCL design. The proposed method is validated by hardware-in-the-loop simulations conducted on PLECS RTBOX 3. Through experimental testing, it is shown that the controller is robust against a wide range of grid impedance variation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".