Improved Predictive Current Controlled PWM for Single-Phase Grid-Connected Voltage Source Inverters
Bibliographic record
Abstract
Inverter-based distributed generators (DG) must meet the power quality requirements set by interconnection standards such as IEEE Standard 1547 before DGs are allowed to interconnect with existing electric power systems. The power quality is highly dependent on the control strategies of the DG inverters. Traditional predictive current controller can precisely control the load current with low distortions, however, has a poor performance under component parameter variations. An improved predictive current controller has been developed by the authors for single-phase grid-connected voltage source inverters (VSI). Aiming to overcoming the drawbacks of the traditional predictive controller, a scheme for improving the robustness of inverter system is proposed along with a dual-timer control strategy and a software phase-lock-loop (PLL). The controller is designed not only to minimize the control error introduced by the control delay but also to provide a faster response for over-current protection. The simulation and experiment results show that the improved predictive controller has a superior performance to the traditional predictive controller, particularly under parameter variations. The single-phase grid-connected VSI implemented with the proposed predictive controller has shown very low current THD in both laboratory tests and in field operation of a small wind turbine system
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".