Extended Modulus Optimum Method for Off-Grid Inverter’s Voltage Control System
Bibliographic record
Abstract
Stationary reference frame proportional-resonant (PR) controllers play a significant role in grid-tie and off-grid inverter control because of their ability to achieve zero steady-state error at AC frequency and provide high rejection ratio for undesirable harmonic content with minimum computation burden. While extensive efforts have been put into application-oriented performance optimization for grid-tie application, the off-grid inverter case was barely investigated. This paper proposes an extension of the Modulus Optimum tuning method from a synchronous frame to a stationary frame. The proposed approach is directly applied to the transfer function of the off-grid inverter in a stationary frame. As a result, the structure of the controller with optimized transient and steady-state behaviours is synthesized. The relationship between the proportional and the resonant part of the investigated controller is obtained to achieve the optimum utilization of the presented control bandwidth. The validation of the presented approach was performed by computer simulations and laboratory experiments on the off-grid battery inverter.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".