Comparative analysis of closed-loop current control of grid connected converter with LCL filter
Bibliographic record
Abstract
Voltage source inverters (VSIs) with output LCL filters are the key interfaces for today's distributed energy resource. There are mainly two groups of current control methods of a VSI: direct error tracking control with PWM, and closed-loop feedback control. Direct current error control, such as predictive control and hysteresis control, has some drawbacks like system parameter sensitivity, variable switching frequency, etc. On the other hand, the closed-loop feedback control could eliminate many drawbacks of direct error tracking PWM method while with the limited of control bandwidth. Closed-loop current control of a VSI can be of two types namely single-loop and multiple-loop VSI control. According to the feedback currents or number of current sensors used, the closed-loop current control can also be classified into single current sensor and two current sensors feedback system. The stability and dynamic performance of these control schemes differs from each other. However, a thorough understanding of the differences and the reasons behind is not available. This paper presents a comparative analysis of different closed-loop current control method for a VSI with output LCL filters. Effect of LCL filter parameter variation on their stability is investigated. Recently proposed generalized closed-loop control (GCC) platform is used to explain the comparison results. Simulation and experimental results of different VSI control systems are presented.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".