Modular active power-line conditioner
Bibliographic record
Abstract
In this paper, a new modular approach to active harmonic filtering is proposed. The method utilizes two linear adaptive neurons (ADALINEs) to process the signals obtained from the line. The first ADALINE (the current ADALINE) extracts the harmonic components of the distorted line current signal and the second ADALINE (the voltage ADALINE) estimates the fundamental component of the line voltage signal. The outputs of both ADALINEs are used to construct the modulating signals of a number of current-source inverter (CSI) modules, each dedicated to eliminate a specific harmonic. The power rating of the modules will decrease and their switching frequency will increase as the order of the harmonic to be filtered is increased. The overall switching losses are minimized due to the selected harmonic elimination and balanced "power rating"-"switching frequency" product. Power losses are also reduced by adjusting the I/sub dc/ in each CSI module according to the present magnitudes of the individual harmonics to be filtered. Speed and accuracy of ADALINE, self-synchronizing harmonic tracking, optimum I/sub dc/ value and minimal converter losses, high reliability and flexibility and speed and low DC energy requirement of the CSI, result in good performance of the proposed active conditioner. The theoretical expectations are verified by digital simulation using EMTDC simulation package.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".