Comprehensive Machine Learning-Based Fault Detection Strategy for Gird-Tied Microgrids Integrating Renewable Energy Sources
Bibliographic record
Abstract
Summary-Hybrid renewable energy systems (HRES), integrating photovoltaic (PV) farms, wind turbines, and battery storage, are revolutionizing modern power generation by offering sustainable and reliable energy solutions. However, their inherent complexity poses significant challenges in fault detection and protection, as traditional methods, such as over-current relays, often fall short, leading to system disruptions. This paper proposes an ML-based fault detection framework utilizing advanced machine learning algorithms and minimal hardware—one voltage sensor on the DC link and one per AC phase. A simulated hybrid system comprising a 500 kW PV farm, 250 kW wind farm, and 200 kW battery storage, connected to a 25 kV grid, evaluates the framework’s efficacy. The results demonstrate 98.14% fault detection accuracy on the AC side and flawless accuracy on the DC side. By leveraging the robustness and precision of machine learning techniques, this framework enhances the reliability, scalability, and operational efficiency of HRES, ensuring robust fault management in modern energy systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".