Hybrid AC/DC System Harmonics Control Through Grid Interfacing Converters With Low Switching Frequency
Bibliographic record
Abstract
The nonlinear loads in distribution grid may generate harmful low-order harmonics, polluting the grid and deteriorating the voltage quality particularly when the grid is weak. The grid interfacing converters in the distribution system, such as the distributed generation (DG) interfacing converters or hybrid ac/dc grid interlinking converters, can participate in distribution grid harmonic control. In this paper, two virtual-impedance-based harmonics control methods are developed for grid interfacing voltage-source inverters (VSIs) to improve the power quality of the distribution grid. As the control parameters are designed based on the virtual impedance theory, clear physical meanings are given to explain their impacts on the system. Also, the proposed methods do not rely on the closed loop feedback control and, therefore, are very suitable for VSIs with low switching frequency (such as those for high-power DGs or interlinking converters for hybrid ac/dc systems), whose closed-loop control feedback bandwidth may be limited for harmonic regulation. The influence of system delay and feedback control loop is considered and modeled in the design procedure; thus more accurate virtual impedance control is achieved for low-switching-frequency VSIs. Moreover, a comprehensive comparison of the two methods, including stability and harmonic compensation performance, are given. Their effectiveness is verified by experiment results.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".