Adaptive active power line filter for interfacing wind-power DGs to distribution system
Bibliographic record
Abstract
Harmonic disturbances caused by power electronic converters for wind-power distributed generation (DG) that may have serious impacts on the power quality of its connecting power distribution systems must be well controlled according to IEEE 1547 Standards. This paper presents a novel adaptive harmonic detection active (AHDA) filter, consisting of an industry-type power electronic inverter controlled by an adaptive noise cancellation algorithm implemented using state-of-the-art digital signal processor. This AHDA filter provides effective elimination of harmonic disturbances generated by the wind-power DG converter, owing to its efficient adaptive harmonic detection algorithm. The algorithm is based on a novel noise cancellation theory, originally not designed for power applications. This paper presents a practical formulation of the algorithm for utility applications that significantly simplifies the complex formulation originally for noise cancellations. Hardware and software implementation of the AHDA filter is detailed. Simulation and experimental results are provided to demonstrate effectiveness of this filter.
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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".