Investigating the performance of non‐standard characteristics‐based overcurrent relays and their optimum coordination in distributed generators connected networks
Why this work is in the frame
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Bibliographic record
Abstract
Abstract To handle the challenges attributed to the distributed generators, the protection system of distribution networks needs to be evolved. Recent protection schemes modify the standard characteristics of overcurrent relays, called non‐standard characteristics. These non‐standard characteristics are mostly case‐dependent and have limitations in solving the variety of protection challenges attributed to distributed generators. This paper analyses the performance of six voltage‐based non‐standard characteristics on photovoltaic distributed generators (PVDG) penetrated 3‐bus, 8‐bus, and IEEE 30‐bus distribution grid, as well as the 9‐bus Canadian radial distribution grid with synchronous distributed generator, wind turbine generator, and PVDG penetrations. Besides that, the optimum coordination of these non‐standard characteristics‐based overcurrent relays is a complex problem due to the involvement of additional controlling parameters. To find the best suitable method, a comparative analysis of the genetic algorithm, differential evolution, artificial bee colony, harmony search algorithm, firefly algorithm, cuckoo search algorithm and particle swarm optimization are performed for optimum relay coordination. The outcomes of the paper are thoroughly discussed for future research on non‐standard characteristics development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it