Frequency-adaptive current controller for grid-connected renewable energy systems
Bibliographic record
Abstract
An adaptive proportional-resonant (APR) controller for current controller of grid-connected DC/AC systems for renewable energy applications is presented. The increase in renewable energy on the electricity grid poses stability risks to the network as the frequency and amplitude are not as tightly regulated as in previous years. The proposed APR controller is able to maintain high quality current at frequencies well beyond the IEEE1547 standard. The controller is constructed in two parts, a phase-locked loop (PLL) for synchronization and identification of the grid frequency, and a current controller based on the form of the well-known PR controller. The current controller is able to dynamically adjust to the shifting frequency of grid which ensures high quality, unity power-factor across the 59.3-60.5Hz spectrum set by IEEE1547, and beyond. The PLL implemented is an amplitude-decoupled adaptive notch filter (AANF) which provides a very fast, an accurate solution for frequency estimation as well as reference signal generation. The AANF and the APR controller are implemented digitally on an FPGA platform and verified experimentally on a 1kW DC/AC prototype.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".