Real-time laboratory implementation results of an active filter
Bibliographic record
Abstract
Proliferation of power electronics converters and electronic equipments has dramatically increased electric pollution in electrical distribution power systems. One solution to this problem could be the use of active filters that are capable of injecting distorted currents in order to cancel harmonics coming from non-linear loads for instance, or from any other equipment. Consequently, many theories have been developed to control active filters and then perform compensation of unwanted harmonics. In this paper, an overview of the instantaneous compensation theory is presented. This time domain approach was proposed by Akagi [1] under the name “p-q theory” or “instantaneous power theory”. It is the most widely spread theory for two or three phases and three or four wire systems. Then an appropriate control model for active filters based on the literature review is presented. That model is interfaced with real physical system owing to Opal-RT Software that definitely allows real-time control. Laboratory implementations are realized by using RT-Lab Simulator, and by interfacing it with a three phase 120/240 V system and a three phase non-linear load. The final system constitutes a Hardware in the loop (HIL) application. Real-time tests on an Opal-RT simulator, are shown and commented in the paper.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".