Voltage-Sag Origin Detection in Smart Grids for Enhanced Resiliency
Bibliographic record
Abstract
The prompt and precise identification of voltage-sags in smart grids is crucial to manage voltage-sag mitigation, system restoration, and recovery effectively while ensuring the security and resiliency of the grid. Given that faults are a primary cause of voltage-sag events, accurately detecting these faults can significantly speed up the mitigation and recovery process. However, as the adoption of Inverter-based Resources (IBRs) increases, traditional fault-detection schemes have become inadequate because they overly depend on evaluating fault currents, which are limited by these IBRs. This paper introduces a methodology for detecting and identifying the origin of voltage sags, enabling the determination of whether a fault is located upstream or downstream from the system's PCC. The simulation results show that the technique is faster and more efficient compared to existing methods in the literature. The proposed fault detection algorithm utilizes a voltage/current estimation technique in combination with Tellegen's theorem to pinpoint the accurate geographical location of the voltage-sag origin in Active Distribution Systems (ADSs). A case study is conducted on a modified IEEE 33-bus distribution network with a three-phase balanced 12.66 kV system, incorporating IBRs throughout the network, to evaluate the algorithm's effectiveness.
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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) | 0.000 | 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 | 0.001 |
| 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".