Supervised Machine Learning and Current Signal Based Universal Fault Protection Strategy for Distributed Generation Integrated into Power Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Distributed Generations (DGs) play a significant role in the modern electric power grid, offering benefits such as reduced transmission and distribution losses and minimized environmental impact. Globally, solar Photovoltaic (PV) and Wind Turbine Distributed Generators (WTDGs) have become some of the most widely utilized DG resources, driven by their improving efficiency, and advancing technology. However, from the viewpoint of power system protection, the integration of these resources into power systems poses various significant challenges for protective relays. These relays traditionally rely on both voltage and current measurements and and may not operate correctly in the presence of WTDGs. To address these issues, the use of a current-only protection system with a unified supervised Machine Learning (ML) classifier for directional and phase selection relays is proposed in this work. The comparative analysis shows the superiority of the Random Forest classifier in using only currents and being able to detect both the fault direction with perfect accuracy and the faulty phase(s) with over 92% accuracy, making it suitable for implementation in both transmission and distribution networks, including WTDGs resources.
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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 | 0.000 | 0.000 |
| 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 it