Adaptive and intelligent control applications to power system stabiliser
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Adaptive control can be described as the changing of controller parameters online based on the changes in system operating conditions. Adaptive controllers based on analytical techniques can provide excellent performance and improve the dynamic performance of the plant by allowing the parameters of the controller to adjust as the operating conditions change. Proper care needs to be taken to make them robust, especially under large disturbances. Controller robustness can be improved by employing artificial intelligence (AI) techniques. It is possible to implement either the entire algorithm using AI techniques or by integrating analytical and AI techniques such that some functions are performed using analytical approach and the rest using AI techniques. Successful implementation of purely analytical, purely AI and integrated approaches is illustrated by application to a power system stabiliser to improve damping and stability of an electric generating unit.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it