Control and Energy Management of Hybrid Renewable DC Microgrid by Using Flatness Method with Predictive Neural Network and Fuzzy PI Regulation
Bibliographic record
Abstract
This article proposes a flatness-based control for a renewable hybrid microgrid comprising a photovoltaic (PV), wind turbine (WT), and battery storage systems.The flatness approach generates reference trajectories for DC-link energy voltage regulation and coordinated power sharing.To increase robustness under renewable intermittency and improve reference-trajectory tracking, the proposed control law is augmented by incorporating a predictive neural network (PNN).In addition, a Fuzzy-PI controller is used in the inner converter loops, where fuzzy logic adaptively tunes the PI parameters in realtime based on the error and its variation.A maximum power point tracking (MPPT) technique based on a perturb and observe (PO) was used to maximize the PV's power.The proposed system was tested in a simulation environment based on MATLAB/Simulink.The obtained results show that the proposed strategy ensures efficient energy management in hybrid microgrids, decreases perturbations in the regulated DC bus, and improves robustness against load variation uncertainties.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".