A Control Scheme for PWM Voltage-Source Distributed-Generation Inverters for Fast Load-Voltage Regulation and Effective Mitigation of Unbalanced Voltage Disturbances
Bibliographic record
Abstract
This paper presents a control scheme for grid-connected pulsewidth-modulated voltage-source inverters (VSIs) featuring fast load-voltage regulation and effective mitigation of unbalanced voltage disturbances. To ensure perfect regulation of the voltage at the point of common coupling (PCC) and provide means for rejecting fast and dynamic voltage disturbances, the frequency modes of the disturbances to be eliminated should be included in the stable closed-loop system. Toward this, a hybrid voltage controller combining a linear with variable-structure control element is proposed for an inverter-based distributed-generation interface to regulate the voltage at the PCC. The proposed voltage controller can embed a wide band of frequency modes through an equivalent internal model. Subsequently, a wide range of voltage perturbations, including capacitor-switching disturbances, can be rejected. To account for unbalanced voltage disturbances, a dual-sequence voltage controller is proposed. To provide accurate and robust tracking of the generated active and reactive current trajectories, a newly designed deadbeat current control algorithm is proposed. The controller is designed under the practical considerations of inherent plant delays, which are associated with the digital implementation of the control algorithm, and the uncertain nature of the current dynamics. Theoretical analysis and comparative evaluation tests are presented to demonstrate the effectiveness of the proposed control scheme.
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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".