Evaluation of islanding detection techniques for inverter-based distributed generation
Why this work is in the frame
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Bibliographic record
Abstract
In this paper; four islanding detection techniques for inverter-based distributed generator (DG) are presented. The techniques are: decision tree (DT), support vector machine (SVM), radial basis function network (RBF), and probabilistic neural network (PNN). In literature, these techniques were proposed as islanding detection methods. However, the proposed techniques face various limitations such as the size and type of the used distribution network and the limitation of the extracted features. This paper overcomes these limitations and gives a very accurate comparison between these techniques by extracting seven features from damped-sinusoid model of the voltage and frequency waveforms using the MATLAB/SIMULINK and also using the IEEE 34-bus distribution system. The results show that out of the four tested techniques, PNN technique can accurately detect islanding for inverter based DG.
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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 | 0.000 | 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