Loss-of-Voltage Detection for Relays Protecting Systems With Inverter-Based Resources
Bibliographic record
Abstract
Reliable voltage measurement is a pre-requisite for correct operation of a relay's voltage-dependent functions. The secondary circuit of a relay's voltage transformer (VT) is usually fuse-protected. The blowing of a VT's three-phase fuses prevents correct voltage measurement, commonly referred to as the loss-of-voltage (LOV) condition. Relays are equipped with different methods to detect the LOV conditions and avoid confusing them with grid faults. However, this paper shows that existing LOV detection methods are prone to misoperation in the presence of inverter-based resources (IBRs). The paper's findings are corroborated by hardware-in-the-loop testing of relays with the state-of-the-art LOV detection methods. This paper also develops a new LOV detection method for transmission system relays that addresses the unveiled problem. The proposed method considers the fault behavior of IBRs and detects LOVs by examining certain conditions in three stages, namely phase-voltage-check, phase-current-check, and current-transients-check. Simulation studies are used to verify the dependable and secure operation of this new method for different LOVs, faults, IBRs, and VT configurations. The obtained results demonstrate that this method performs successfully regardless of the type of generation units. Moreover, it is sufficiently fast to prevent relay misoperation during LOV conditions and does not require extra hardware.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".