Dynamic Threshold-Based Event-Triggered Strategy for Robust Fully Distributed Control in Renewable-Powered DC Microgrids
Bibliographic record
Abstract
This paper presents a novel Fully Distributed Adaptive Observer-Based Event-Triggered (FDAOET) secondary control strategy for islanded DC microgrids (MGs) that achieves accurate voltage restoration and proportional current sharing while optimizing communication efficiency without compromising system resilience. Unlike conventional static or heuristic event-triggered (ET) schemes, and even existing dynamic ET methods that rely on fixed structures or global parameters, the proposed strategy employs an observer-based, state-aware triggering mechanism. Each distributed generator (DG) transmits data only when the deviation between its internal observer estimate and local measurement exceeds an adaptively evolving threshold governed by an ordinary differential equation (ODE). This formulation enables robust, context-aware communication scheduling that maintains estimation accuracy under noise, parameter uncertainties, and network variability. Additionally, the proposed method explicitly incorporates bounded communication and actuation delays into both the triggering and control design, ensuring reliable operation under realistic conditions. The FDAOET strategy is fully distributed and topology-independent, requiring no global information such as Laplacian matrices, thereby supporting plug-and-play scalability. Zeno behavior is rigorously avoided, and system stability is proven using Lyapunov methods. Simulation results in MATLAB/SimPowerSystems demonstrate superior performance in terms of convergence speed, voltage accuracy, current sharing, and significant reduction in communication events compared to static, dynamic, and delay-unaware ET methods.
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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 | 0.001 |
| 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 | 0.001 |
| 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".