A new single phase hybrid passive filter to dampen resonances and compensate harmonics and reactive power under distorted source conditions
Bibliographic record
Abstract
This paper proposes a new single phase hybrid passive filter (SPHPF) for compensating load voltage and current harmonics, correct power factor, eliminate the chances of series and parallel resonance and eliminates large variation of power factor and terminal voltage with varying loads under stiff and distorted source conditions. The harmonics analysis is performed in order to observe the percentage reduction of total harmonic distortion for passive filter at various loads. The investigations are carried out to validate the performance of proposed SPHPF with detuned resonance, for effective and efficient operation under varying loads and source conditions. The topology of SPHPF is chosen to shift the resonance frequency of the resulting system below 80 Hz where no excitation is expected. The frequency response shows excellent damping characteristic for load harmonic current above 80 Hz, which indicates that the hybrid passive filtering performance of this topology is satisfactory. Experimental and simulation results show that the proposed passive filter can simultaneously compensate effectively all voltage and current harmonics and reactive power for large capacity nonlinear load.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".