DNN-based On-the-Fly Adaptive Overcurrent Protection for Grid Mode Transitions in AC Microgrids
Bibliographic record
Abstract
AC microgrids with high distributed energy resources (DERs) penetration frequently transition between grid-connected and islanded modes, causing significant variations in power flow, network topology, and fault levels. These dynamics challenge conventional overcurrent protection, as fixed-setting relays lack adaptability and offline-adaptive relays depend on manual updates, leading to unreliable coordination and delayed fault isolation. This paper proposes a deep neural network (DNN) based on-the-fly adaptive overcurrent protection scheme that continuously monitors real-time system data, including fault currents and rate-of-change-of-frequency (ROCOF) element, to detect mode transitions and autonomously update relay settings, including pickup current and time dial. By incorporating the ROCOF element, the DNN-assisted relays maintain selectivity and enable rapid fault clearing amid sudden system changes. Simulation studies on a modified CIGRÉ AC microgrid demonstrate that the proposed method outperforms conventional protection in speed and accuracy.
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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 | 0.001 | 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 | 0.001 |
| 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".