Harmonic compensation in ac distribution systems using smart electronic loads with PFC converters
Bibliographic record
Abstract
The number of electronic loads in ac distribution systems is rapidly increasing, and some of such nonlinear loads are injecting harmonics and reducing the power quality. At the same time, there are electronic loads that are equipped with high-bandwidth converters and power factor correction (PFC), which inject sinusoidal currents at line frequency back into the grid. Moreover, modern smart meters have the capability of measuring higher-order harmonics in addition to power. This paper proposes and investigates using the smart electronic loads with PFCs for compensation of harmonics in distributed power systems. Such smart loads may communicate with each other and the smart meter for monitoring and control of power quality in a building or community distribution systems and microgrids. Due to the topology and hardware constraints, the traditional PFCs cannot achieve ideal harmonic compensation. This paper analyzes the operation of PFC converter and proposes a new approach to broaden the range of harmonic compensation. Simulation and experimental results are presented based on a commercial PFC prototype to validate the proposed approach.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".