A fuzzy logic based controller of DC-link voltage regulation of parallel inverters operation during unintended islanding mode
Bibliographic record
Abstract
This paper investigates the effects of unintended island operation mode on parallel inverters' performance for microgrid applications. In the grid-connected mode, each inverter has a different power set point. The power flows between the inverters and the grid. In the islanding mode, the power flows inherently from the inverter that has higher set point power to the inverter has lower one leading to a substantial and rapid increase in dc-link voltage. Consequently, a controller is needed to protect the inverters from highly dc link voltage (HDLV) that may lead to fatal damages of the capacitor and power switches. However, the use of a dc-link controller for the second inverter makes unstable the first inverter dc-link voltage regulation. This work proposes a fuzzy logic controller (FLC) designed to avoid any rising in DC link voltage DLV for the second inverter and make it more stable. The effectiveness and performance of the proposed control method are demonstrated with numerical simulations carried out for three operation scenarios. The first one deal with two inverters operating in islanding mode where each inverter has a different set point P* <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> = 20 kW, and P* <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> = 0 kW. The second scenario considers two inverters operating in grid connected mode before being switched to the unintended islanding mode at a specific time without using dc-link voltage controller. And finally the third scenario investigates the effect of adding a dc-link voltage controller (DLC) to the system.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".