Interactive Distributed Generation Interface for Flexible Micro-Grid Operation in Smart Distribution Systems
Bibliographic record
Abstract
This paper presents an interactive distributed generation (DG) interface for flexible micro-grid operation in the smart distribution system environment. Under the smart grid environment, DG units should be included in the system operational control framework, where they can be used to enhance system reliability by providing backup generation in isolated mode, and to provide ancillary services (e.g. voltage support and reactive power control) in the grid-connected mode. To meet these requirements, the proposed flexible interface utilizes a fixed power-voltage-current cascaded control structure to minimize control function switching and is equipped with robust internal model control structure to maximize the disturbance rejection performance within the DG interface. The proposed control system facilitates flexible and robust DG operational characteristics such as 1) active/reactive power (PQ) or active power/voltage (PV) bus operation in the grid-connected mode, 2) regulated power control in autonomous micro-grid mode, 3) smooth transition between autonomous mode and PV or PQ grid connected modes and vice versa, 4) reduced voltage distortion under heavily nonlinear loading conditions, and 5) robust control performance under islanding detection delays. Evaluation results are presented to demonstrate the flexibility and effectiveness of the proposed controller.
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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".