Smart meter based selective harmonics compensation in buildings distribution systems with AC/DC microgrids
Bibliographic record
Abstract
Preserving the power quality of hybrid AC/DC systems becomes more challenging as the number of loads and interconnections increases. To improve the power quality, several selective harmonics elimination methods and active filtering devices have been presented in the literature; however, their implementation costs are relatively high. Moreover, most of the proposed solutions require changing/modifying the existing grid infrastructure, such as installing current transducers at the point of common coupling (PCC). In this paper, a harmonic compensation method is proposed for hybrid AC/DC microgrid systems based on smart meter monitoring, which is applicable to existing building-scale microgrids and can reduce the costs significantly. The feasibility of the proposed harmonic compensation method is investigated based on the smart meter data for a typical building at The University of British Columbia (UBC) campus. The simulation results verify the effectiveness of the proposed method for reducing selected harmonics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".