A model predictive control approach for integrating a master generation unit in a microgrid
Bibliographic record
Abstract
This paper presents a model-based fault-tolerant approach for designing a control strategy in order to integrate a diesel engine generator (DEG) as master generation unit, voltage and frequency leader, in an islanded microgrid configuration. The microgrid design is mainly composed of a hybrid wind-diesel-photovoltaic power system with a battery storage system (BSS). A model predictive control (MPC) scheme has been selected for this task, due to its flexibility and capability for handling constraints. Fault-tolerance is achieved in the DEG control system with the addition of a fault detection and diagnosis (FDD) module to the MPC structure, in order to reconfigure the control strategy when actuator faults in the DEG are present. Different operating conditions of the microgrid were simulated in order to test control robustness. Improved performance over a baseline controller, IEEE type 1 exciter, is achieved. Dynamic models of the microgrids components are presented and simulation results of the microgrid behavior in Matlab/Simulink <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> .
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".