Fault Ride Through of Inverter-Interfaced Renewable Energy Sources for Enhanced Resiliency and Grid Code Compliance
Bibliographic record
Abstract
Inverter-interfaced renewable energy sources (IIRESs) are typically controlled during fault conditions to meet fault ride-through (FRT) requirements, e.g., reactive current generation (RCG) requirements specified by grid codes (GCs). However, fault currents generated by inverters are different from the traditional sources, i.e., synchronous generators. Consequently, phase selection methods (PSMs) used by protection relays could suffer from erroneous fault type classification. This paper develops a dual current controller (DCC) that regulates the inverter's negative- and positive-sequence currents to simultaneously meet phase selection and RCG requirements. First, the negative-sequence-current angle is obtained based on the angles of both zero- and positive-sequence currents to enable a correct operation for phase selection. Then, the positive-sequence current angle is adjusted to reach a trade-off between RCG requirements and phase selection achieved by the negative-sequence current. Lastly, the reference currents of the IIRES are generated in the stationary frame without violating the inverter's current limits. The proposed DCC supports the grid voltage by meeting the RCG requirements and enhancing the grid reliability and resilience by enabling correct phase selection. Comprehensive time-domain and real-time simulation verify the precise operation of the proposed DCC under various fault conditions and GCs.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".