Protection of systems containing IBR from asymmetrical ground faults using zero sequence current with hardware implementation on FPGA
Bibliographic record
Abstract
Abstract The addition of inverter‐based generation to the power system complicates protective relaying in several ways: the magnitude of the fault current will be altered based upon the location of the fault relative to the inverter‐based resource, and the response to a fault will be different than traditional generation. The relatively low fault current of inverter‐based resources presents difficulties in differentiating faults from increased loads. Additionally negative sequence relays may fail in the presence of IBR's due to the negligible negative sequence current produced by the IBR during a fault. This paper presents a novel method to allow the sensitive sub‐cycle detection, and determination of the relative location, of asymmetrical ground in systems containing inverter‐based resources through phasor analysis of zero sequence current. Least Error Squares Estimation is applied to the fault currents to improve the detection time of these faults, with low computational overheard for hardware implementation. Hardware implementation of the protection scheme on an Intel Cyclone IV‐E FPGA is completed and correct operation of the scheme is verified for the raw samples, as well as five and seven sample LES phasor estimations. Hardware testing is compared to the EMT output to confirm the results.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".