A Comprehensive Dual Current Control Scheme for Inverter-Based Resources to Enable Correct Operation of Protective Relays
Bibliographic record
Abstract
Directional, phase-selection, and distance elements of a relay are prone to misoperation in the presence of inverter-based resources (IBRs). To address these problems, modifications of relay elements as well as new control schemes for IBRs have been proposed in the literature. These methods focus mainly on addressing the misoperation of only one of the above relay elements. The available solutions that are based on a new control system focus only on the negative-sequence current loop. Thus, they work only for unbalanced faults. This paper proposes a comprehensive dual current control scheme for IBRs during all types of faults such that all of these relay elements operate correctly. The proposed method controls the IBR to operate as a voltage source behind a virtual impedance in the positive-sequence circuit, and a single impedance in the negative-sequence circuit, thereby emulating the phase angles of a synchronous generator's (SG's) fault current. The proposed method also maximizes the active, and reactive current generation of the IBR during faults, and emulates the incremental sequence current magnitudes of an SG. The proposed method does not require altering the relay elements. Moreover, correct operation of the relay elements is achieved regardless of the fault type, resistance, and location. The effectiveness of the proposed method is verified through not only simulation studies, but also hardware-in-the-loop testing of commercial relays.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".