Model Predictive Control for LCL-Filtered DG-Grid Interfacing Inverters With State Variable and Input Disturbance Estimation
Bibliographic record
Abstract
Due to the high-order property and multivariable coupling characteristics of LCL filtered voltage source inverter (VSI), model predictive control (MPC) have received great favor with the advantage such as the intuitive concept, improved bandwidth, and multiobjective optimization. To suppress the inherent resonance problem, reject input disturbance and meet the multifunction requirement in distributed generation (DG) units, a model-based estimator is proposed in this paper, which exploits the previous four consecutive output information to simultaneously calculate the value of state variable and input disturbance in real-time. Specifically, with only grid voltage and current measurement, the inverter-side current, capacitor voltage, and input voltage disturbance can be obtained, which are used to generate the optimized control law. Constant switching frequency is achieved thanks to the modulator. Therefore, the proposed control law can improve the injected power quality, and also the system hardware cost can be saved. Simulation and experiment are realized to verify the proposed method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".