Experiences in system voltage monitoring and control in evolving power grid and application of control room tools
Bibliographic record
Abstract
System voltage control has been a challenging task in the BCHydro power system due to generating resources located far from load centers. The system is supported by emergency and quasi emergency measures such as load shedding and auto-var schemes. In addition, operating orders cover extensive voltage control measures. Due to recent changes in electric power system structure and market driven operational changes electric power system operation has increased in its complexity. Historically Control Room staff have been supported by off-line tools in the past progressively increasing the use of tools in the control room. Use of emergency schemes supported by real time tools has been an approach matured over several years of use. More recent are the upgrading the stability tools with technological progress and use of formal optimization tools as well. The authors share their experiences of managing system voltages including normal and emergency measures and also procedures such as auto-var schemes, automatic under-voltage load shedding schemes supported by tools in EMS to arm the load shedding schemes, formal optimization tools, etc., with the technologies that are mature enough for use in control room.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".