Modeling and Stability Analysis of Single-Phase Microgrids Controlled in Stationary Frame
Bibliographic record
Abstract
Modern microgrids are transitioning toward having an increasing portion of grid-forming (GFM) converters to support nontraditional sources, such as renewable energy and energy storage systems, while aiming to improve system stability, reliability, power rating, and flexibility. Understanding the interaction among parallel GFM converters to guarantee the microgrid stability within various operating conditions is necessary. For single-phase systems, when the controllers are implemented in the rotating frame to facilitate the stability analysis and design, the required orthogonal signal generation (OSG) units degrade the system stability. On the other hand, when the controllers are implemented in the stationary frame to avoid OSG-related problems, the stability analysis becomes particularly challenging due to nonlinearities and mixed dc and ac state variables. In this article, an approach is proposed to systematically model and perform stability analysis for a single-phase microgrid with stationary frame controllers. The approach is based on defining a complementary system that allows transformation to the synchronous rotating frame without introducing time-varying (double-frequency) terms and without altering the stability properties. The accuracy of the proposed modeling approach is verified using simulations and experimental results.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".