Bibliographic record
Abstract
Traditionally, a microgrid relies on electro-mechanical generators to achieve reliable and stable operations.With recent technical advancement in the fields of renewable energy and energy storage, a modern microgrid can operate autonomously with only inverter-based energy sources.While the three-phase microgrid has been extensively studied in recent years, the single-phase microgrid has been barely investigated.However, massive numbers of single-phase microgrids (or nanogrids in some references and this thesis) are now being deployed in conjunction with the introduction of electric vehicles (EVs) and household power generators (such as photovoltaic solar) to the 130 million single-phase consumers in North America.The lack of single-phase microgrid analysis represents a significant knowledge gap.The objective of this thesis is to advance the understanding of single-phase nanogrids consisting of both grid-supporting and grid-forming inverters.Three topics are covered in this thesis.• Integration of the EV in the nanogrid: a novel control and optimization strategy for On-board Battery Charger (OBC) is proposed.It is based on Direct Current Hysteretic Control (DCHC) with optimized switching patterns and dead time.The strategy can significantly reduce the switching losses of Silicon Carbide (SiC) based inverters.• A voltage control loop tuning approach for grid-forming inverters: by extending the Modulus Optimum algorithm to the stationary reference frame, the proposed approach provides a simple and reliable method when tuning the voltage control loop.iii• A systematic single-phase modelling methodology: a synchronous dq0 frame model of the single-phase inverters is developed.It allows the application of three phase stability analysis techniques/tools to single phase systems.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".