Dual Current Control of Renewable Energy Sources for Recent Grid Code Compliance and Reliability Enhancement
Bibliographic record
Abstract
Inverter-interfaced renewable energy sources (IIRESs) are controlled to follow low-voltage ride-through requirements during different fault conditions to support grid voltages and enhance stability. However, these requirements could result in improper operation of conventional protection functions, e.g., phase selection, that are designed based on the fault current characteristics of conventional power systems. In this paper, a dual-current controller (DCC) is designed for IIRESs to allow precise operation of the conventional phase selection method (PSM) while following the positive- and negative-sequence reactive current generation (RCG) requirements imposed by recent grid codes (GCs). First, the negative-sequence current reference is designed to comply with GC requirements and inject the minimum value of negative-sequence-active current that secures a correct operation of phase selection. Subsequently, the positive-sequence-reactive current is designed to comply with RCG requirements and allow injecting the maximum combination of the positive-and negative-sequence currents without hindering the proper operation of PSM or RCG requirements. Comprehensive time-domain simulations verify the effectiveness of the proposed DCC in meeting both PSM and recent RCG requirements during different fault types, resistances, and locations.
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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 | 0.000 |
| 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".