Adaptive fuzzy-recurrent neural network tuned fractional-order distributed control for robust frequency regulation in multi-microgrid systems
Bibliographic record
Abstract
This paper presents an advanced frequency control solution for multi-microgrid systems (MMGS) with high renewable energy penetration, where conventional control methods struggle with scalability and disturbance resilience. We propose a Distributed Consensus Control Strategy (DCS) using a Fractional Order PID (FOPID) controller adaptively tuned by a Fuzzy-Recurrent Neural Network (FRNN). Our method delivers significant improvements in both transient and steady-state performance compared to established approaches. Extensive real-time hardware-in-the-loop (HIL) testing on a three-microgrid platform demonstrates the superiority of the proposed controller. In MG1, it reduces settling time by 29% (4.19 s vs. 5.93 s for PID), lowers peak overshoot by over 90% (0.0007 vs. 0.0076 for PID), and cuts absolute error by more than 80% (1.017 vs. 6.32 for PID). Similar improvements are validated across MG2 and MG3, and comprehensive scenario testing confirms the method's robustness under diverse disturbances. The proposed FRNN-tuned FOPID-based DCS stands out as the first adaptive, distributed framework offering real-time self-optimization and exceptional resilience for frequency regulation in MMGS. Its demonstrated performance and adaptability make it a leading candidate for future smart grids and cyber-physical energy systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".