Power Quality Control of Smart Hybrid AC/DC Microgrids: An Overview
Bibliographic record
Abstract
Today, conventional power systems are evolving to smart grids, which encompass clusters of AC/DC microgrids, interfaced through power electronics converters. In such systems, increasing penetration of the power electronics-based distributed generations, energy storages, and modern loads provide a great opportunity for power quality control. In this paper, an overview of the power quality control of smart hybrid AC/DC microgrids is presented. Different types of power quality issues are studied first, with consideration of real-world hybrid microgrid examples, including data centers, electric railway systems, and electric vehicles charging stations. It shows that compared to traditional centralized power quality compensations, smart interfacing power converters from distributed generations, energy storages, and loads, and the AC and DC subgrids interfacing converters are promising candidates for power quality control. To realize the smart interfacing converters’ power quality control, both primary converters control and secondary system coordination are required. In this paper, a thorough review of the primary control of interfacing converters to integrate the power quality compensation are presented, with a focus on the hybrid AC/DC microgrid harmonics compensation and unbalance compensation. For multiple interfacing converters, the secondary control with system-level coordination and optimization for harmonics and unbalance compensation (considering both unbalance and harmonics in single-phase and three-phase systems) are also presented. Challenges like low switching frequency of interfacing converters, parallel interfacing converters operation, and interfacing converters communications are discussed, and typical solutions for primary and secondary controls to deal with them are presented. The paper also includes rich case study 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".