A Self-commissioning Notch Filter for Active Damping in a Three-Phase LCL -Filter-Based Grid-Tie Converter
Bibliographic record
Abstract
LCL-filters are a cost-effective solution to mitigate harmonic current content in grid-tie converters. In order to avoid stability problems, the resonance frequency of LCL-filters can be damped with active techniques that remove dissipative elements but increase control complexity. A notch filter provides an effective solution, however tuning the filter requires considerable design effort and the variations in the grid impedance limit the LCL-filter robustness. This paper proposes a straightforward tuning procedure for a notch filter self-commissioning. In order to account for the grid inductance variations, the resonance frequency is estimated and later used for tuning the notch filter. An estimation for the maximum value of the proportional gain to excite the resonance is provided. The resonance frequency is calculated using the Goertzel algorithm, which requires little extra computational resources in the existing control processor. The discrete Fourier transform coefficients are therefore obtained, with less calculations than the running sum implementation and less memory requirements than with the fast Fourier transform (FFT). Thus, the self-commissioning technique is robust to grid impedance variations due to its ability to tune the grid-tie inverter on-site. Finally, the analysis is validated with both simulation and experiments.
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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 | 0.001 |
| 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) | 0.001 | 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".