Development of Voltage Sag Metric for Large Meshed Transmission Systems and Stochastic Fault Data Sensitivity Analysis
Bibliographic record
Abstract
This paper describes an approach to determine the impact zone and voltage sag metric for delivery points (DP) in large meshed transmission systems. The metric is to quantify the severity and frequency of voltage sag (VS) experienced at particular DPs. The network model of the system is developed and verified for voltage sag calculation using fault data captured by the power quality (PQ) meters installed across the system. The validated model is used to form the impact zone in terms of the circuit length causing the voltage sag event. Having the impact zones, stochastic fault data is used to create the voltage sag metric. To show the sensitivity of voltage sag metric to different treatment of stochastic fault data, the voltage sag metric is obtained with two sets of data: individual circuit outage frequency and zonal average outage frequency. The approach is implemented on Hydro One Ontario Transmission system. The results are examined to correlate the system behavior with network configuration, voltage level, and proximity to major stations with auto transformers. The results are validated using the data collected over 3 to 5 years by the PQ meters installed in the system. At utility-scale, the approach is used to proactively predict the impact of new planning projects, the retirement of big generation units, and major system reconfiguration on the voltage sag performance of major stations. Additionally, for customers connected on the low voltage (LV) side of transformer stations, the proposed metric can be used to differentiate the impact of transmission system from the events caused by faults on distribution feeders.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".