FPGA-based implementation of an adaptive notch filter used for grid synchronization of grid-connected converters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents the digital implementation of an adaptive notch filter (ANF) for the application of utility grid synchronization. Due to the nonlinear structure of the ANF, the implementation technique has a very big impact on the accuracy of the frequency estimation and the speed of convergence during transients. In this paper, three different techniques are analysed in terms of the performance and the amount of resources they require to be implemented. A comprehensive qualitative analysis is performed, to compare the different implementation techniques. The outcome of this analysis proposes the most accurate and cost-effective method of implementation for the adaptive notch filter. The performance of the different techniques is investigated by simulation. Also, the different ANF implementation techniques are implemented on a Field-Programmable Gate Array (FPGA)-based experimental prototype of a grid connected voltage source inverter. The experimental results are well aligned with the analytical results acquired from the simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it