Full Feedforward of Grid Voltage for Discrete State Feedback Controlled Grid-Connected Inverter With LCL Filter
Bibliographic record
Abstract
Due to multifeedback of state variables, a discrete state-space controller offers outstanding control bandwidth as well as control stability for popular LCL-type grid-connected inverters, while the grid current is still vulnerable to the grid-voltage harmonics. The feedforward of grid voltage, usually employed in continuous controllers to solve such a problem, is too complicated to be applied for the discrete state-space controller. By means of continuous transformation, a full grid-voltage feedforward (GVFF) decoupling strategy is successfully proposed in this paper to make the feedforward possible for the discrete state-space controller. Based on the transfer function analysis and comparison of discretized and continuous system, comprehensive verification is also provided to verify the effectiveness of the derivation of the GVFF path. Subsequently, the robustness analysis of the proposed strategy to the grid impedance is also performed. Moreover, a grid-voltage estimator instead of the measured voltage is employed for the full GVFF, which not only retains the important information of grid voltage but also eliminates the influence of accompanied noises. The distinct features of the proposed feedforward controller plus implementation strategy are the super steady waveform, dynamic response, and robustness to the variation of grid impedance. Besides, the complexity of the algorithm is moderate and the computational burden is not significantly increased. Finally, simulation and experimental results are provided to verify the feasibility and validity of the proposed strategy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".