Fixed-frequency space-vector-modulation control for three-phase four-leg active power filters
Bibliographic record
Abstract
The paper presents a three-dimensional space-vector-modulation scheme for three-phase four-wire active power filters. The focus is on the implementation of a fixed frequency pulse width modulation (PWM) scheme with a minimum number of switch commutations per period and maximum DC bus voltage utilisation. For three-wire applications, space vector modulation is known to provide better utilisation of the DC voltage compared to a sinusoidal PWM approach. This concept is extended to four-wire applications by employing a four-leg active power filter. The largest symmetrical region in which the active filter's voltage space vector may reside is identified. Restricting the voltage space vector to this region avoids over-modulation and thereby prevents the production of low order harmonics. A digital controller is employed to provide deadbeat current control. The combination of the digital controller and the modulation scheme gives the four-leg active power filter the capability to independently track reference current waveforms in the three phases within one switching period. The four-leg active power filter may be used for harmonic compensation, reactive power compensation, load balancing, and neutral current compensation. Experimental results obtained from a 5 kVA laboratory active power filter validate the proposed modulation scheme as well as the control design.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".