Real‐time hardware‐in‐the‐loop simulation for islanding detection schemes in hybrid distributed generation systems
Why this work is in the frame
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Bibliographic record
Abstract
The increasing penetration level of renewable energy brings new challenges to the field of islanding detection. In this study, a decision‐tree (DT)‐learning method with hardware‐in‐the‐loop (HIL) simulations is proposed to address the non‐detection zone (NDZ) issue of islanding detection in hybrid distributed generation systems including both inverter‐ and synchronous‐machine‐based distributed energy resources. This method can effectively reduce the NDZ through advanced relay training strategy and utilise the advantage of real‐time simulators on simulation efficiency. In addition, the generated DT can be programmed into a real relay and then get validated through HIL simulations. The HIL implementation adds a practical dimension to the proposed method. With the proposed method, the laboratory testing results indicate promising islanding detection performance in terms of dependability, security, reduced NDZ area and detection time.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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