Monitored Unit and System Governing Response to Large Frequency Changes following Loss of Generation in Normal Operation System
Bibliographic record
Abstract
Two electric utility control areas independently adapted existing system data collection software to capture the response of the units within their several utilities' generation before, during, and after the loss of large blocks of generation [ranging from 1000 MW to 2200 MW] during periods of otherwise normal operation. These measurements are essentially unannounced tests of the governing response of the units and of the system response to frequency changes. The results have been examined with knowledge of the primary frequency control systems to provide evidence of the effects of the primary frequency control systems. The two operating areas were AEP (American Electric Power) and NYISO (New York IndependentXSystem Operator); both are members of the US-Canadian Eastern Interconnection (EI). AEP was and is a member of the NERC (North American Reliability Council) and of NERC's ECAR (East Central Area Reliability Council). NYISO was and is a member of NERC and of NERC's NPCC (Northeast Power Coordinating Council). At the time of the measurements AEP had approximately 24,000 MW of installed generation, and NYISO had approximately 33,000 MW of installed generation. This paper is one of several that have evolved from the work of the IEEE PES Task Force on Large Interconnected Power System Response to Generation Governing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".