The Autonomous Stress Indicator for Remotely Monitoring Power System State and Watching for Potential Instability
Bibliographic record
Abstract
The proposed Autonomous Stress Indicator (ASI) is a device that monitors the contents of the protection relays on a suspect weak power system bus and generates a performance level related to the degree of system performance degradation or instability. This gives the system operators some time (minutes) to take corrective action. In a given operating area there would not likely be a need for an ASI on every bus. Note that the ASI does not trip any breakers; it is an INFORMATION ONLY device. An important feature is that the system operator can subsequently interrogate the ASI to determine the factor(s) that led to the performance level that has been initially annunciated, thereby leading to a course of action. This paper traces the development of the ASI which is an ongoing project. The ASI could be also described as a stress-alert device whose function is to alert the System Operator of a stressful condition at its location. The characteristics (or essential qualities) of this device are autonomy, selectivity, accuracy and intelligence. These will fulfill the requirements of the recommendation of the Canada –US Task Force in the August 2003 system collapse. Preliminary tests on the IEEE 39-bus model indicate that the concept has merit and development work is in progress. While the ASI can be applied to all power system operating conditions, its principal application is to the degraded state of the system where the System Operator must act to restore the system to the secure state before it migrates to a stage of collapse. The work of ASI actually begins with the Areas of Vulnerability and ends with the Predictive Module as described in detail in this paper. An application example of a degraded system using the IEEE 39-bus system is included.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".