RLS and Kalman Filter Identifiers Based Adaptive SVC Controller
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Bibliographic record
Abstract
This paper presents a prospective application of a static VAr compensator (SVC) in power systems, with particular emphasis on the use of an SVC with a supplementary adaptive controller to enhance system damping. The SVC adaptive controller consists of an on-line identified system model and a pole-shift (PS) feedback controller. Recursive least squares (RLS) identification algorithm and Kalman Filter as a parameters estimator are used for on-line model identification to obtain a dynamic equivalent model of the system. The two methods are compared to determine the most appropriate identification algorithm for this application. The PS controller is then adapted using the identified model. The proposed technique is tested on a single machine infinite bus system and a fifth-order multi-machine system. The results obtained demonstrate improvement in the overall system damping characteristics by applying the proposed adaptive controller as well as an enhancement of the power system stability in comparison to the conventional controller.
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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