Multi converter approach to active power filtering using current source converters
Bibliographic record
Abstract
Active power filtering is performed by detecting the line current and voltage signals, generating a current equal-but-opposite to the distortion current using a power converter and injecting the compensating current into the power line. In such applications, it is desirable to combine high power and high switching frequency while minimizing the losses. This asks for special converter topologies and control techniques. In this paper, a multi-converter active power-line filter, based on current-source converter (CSC) modules, is proposed. The power rating and switching frequency of each CSC module is equal to those required for the filtering job divided by the number of modules. The control system utilizes two linear adaptive neurons (ADALINE's) 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 ADALINE's are used to construct the modulating signals of the filter modules. The proposed modular active filter offers the following advantage: (1) high efficiency due to low conduction and switching losses; (2) high reliability; and (3) high serviceability. The proposed active power-line filter treats the AC system on a per-phase basis, has fast response and adapts to the load variations. 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".