A Digital Current Control Technique for Grid-Connected AC/DC Converters Used for Energy Storage Systems
Bibliographic record
Abstract
This paper presents an advanced current controller for grid-connected bidirectional ac/dc converters used for energy storage systems (ESSs). The proposed control scheme is designed to incorporate both the time-domain and frequency-domain dynamics to achieve superior transient and steady-state performance. The advanced current controller can overcome the various challenges faced by the conventional current controllers used in this application, which lead to sluggish transients and steady-state errors when tracking the sinusoidal reference for the grid current. Combining the time-domain dynamics with the frequency-domain dynamics creates a more intelligent controller compared to existing methods, which only consider the dynamics of one domain. The proposed controller is able to eliminate steady-state error by adaptively changing the controller coefficients in the frequency domain according to the current error. In the time domain, the control scheme minimizes the derivative of a defined energy function in order to optimize the transient performance. Simulation and experimental results obtained from a 3.3-kW grid-connected ac/dc converter demonstrate its superior performance.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".