An Adaptive Intelligent Strategy for Efficient Fault Detection and Localization in Hybrid Microgrid
Bibliographic record
Abstract
Fault detection and protection is one of the challenging tasks in a power system, especially when integrated with microgrids. This is due to frequent changes in topology and variations in the short-circuit level, which affect the overcurrent grading of the relays. However, machine learning (ML) has been found to be effective in such scenarios. This article proposes an adaptive intelligent fault detection and classification method that dynamically integrates three learning models, adjusting their contributions based on performance under various conditions. This approach simplifies the system by utilizing novel data labeling for fault line detection and localization with a light gradient boosting machine (LightGBM) model, thus reducing complexity and response time. The current, measured as data input, is decomposed using wavelet packet decomposition (WPD). The standard deviation and energy are calculated from the wavelet coefficients, which serve as features for training the models. The proposed method effectively addresses challenges in hybrid microgrids, achieving: 1) 99.35% accuracy in fault detection and classification and 2) 99.99% accuracy in identifying faulty lines and their locations. It offers a precise and adaptable solution for simulated data, outperforming conventional protection strategies.
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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 | 0.001 | 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 | 0.000 |
| 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".