An efficient control of the series compensator for sag mitigation and voltage regulation
Bibliographic record
Abstract
The series compensator is a challenging solution for power quality problems related to the voltage. One of the most common control algorithms used for the series voltage compensator is the symmetrical component method. this paper introduces a new recursive least square (RLS) structure for symmetrical components estimation. This structure is capable of dealing with multioutput (MO) systems for parameter estimation and is called MO-RLS. A novel feed forward control based on the proposed MO-RLS is dictated for the series compensator not only to compensate for the zero and negative sequence components, but also to regulate the positive sequence component to the nominal load voltage. One advantage of the proposed control system is its insensitivity to parameters variation, a necessity for the series compensator. Simulations of the proposed algorithm are conducted to show the robustness, the high accuracy and the fast dynamic performance of the novel system.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".