Bibliographic record
Abstract
Passive filters are widely used to limit harmonic propagation, to improve power quality, to reduce harmonic distortion, and to provide reactive power compensation. These are designed for high-current and high-voltage applications. Many such filters are in operation for HVDC transmission systems, large industrial drives, static VAR compensators, and so on. The passive filters are classified into many categories such as shunt, series, hybrid, single tuned, double tuned, damped, band-pass, and high-pass power passive filters. In high power rating such as HVDC systems, they are very much in use even nowadays due to simplicity, low cost, robust structure, and benefits of meeting reactive power requirements in most of the applications at fundamental frequency. Moreover, they are also extensively used in a hybrid configuration of power filters, where major portion of filtering is taken care by passive filters. In majority cases, shunt passive filters have been considered more appropriate to mitigate the harmonic currents and partially to meet reactive power requirement of these loads and to relieve AC network from this problem, especially current-fed types of nonlinear loads (thyristor converters with constant current DC load). However, in voltage-fed types of loads (diode rectifiers with DC capacitive filter), passive series filters are considered better for blocking of harmonic currents. There are many situations that need power passive filters but with varying configurations; therefore, an exhaustive study of the power passive filters is considered very much relevant and presented here.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.035 |
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".