A Flexible Harmonic Control Approach Through Voltage-Controlled DG–Grid Interfacing Converters
Bibliographic record
Abstract
The increased penetration of nonlinear loads and power-electronics-based distributed generation (DG) systems may introduce power quality issues to the distribution power system. However, if controlled and regulated properly, the DG–grid interfacing converters are able to improve the distribution system efficiency and power quality. This paper focuses on the distribution system harmonic control through the DG–grid interfacing converters. Two alternative DG systems, namely, current-controlled DG and voltage-controlled DG, are considered. While most of the previous works on harmonic compensation are based on the current-controlled method, a novel harmonic control scheme using a voltage-controlled method is developed in this paper. The voltage-controlled method is more flexible and has similar compensation performance compared to the conventional current-controlled method. In addition, by avoiding the implementation of a harmonic current tracking loop, the proposed voltage-based method can be seamlessly incorporated into a voltage-controlled DG unit, which is important to provide direct voltage and frequency support in a microgrid. Moreover, the possible harmonic circulating current among multiple DG systems is also investigated in this paper. Simulations and experimental results from a three-phase 5-kVA laboratory DG prototype are provided to validate the discussion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".