A New Algorithm for Busbar Fault Zone Identification Using Relevance Vector Machine
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Bibliographic record
Abstract
This article presents relevance vector machine (RVM) based relaying scheme for busbar protection which correctly differentiates between internal faults on busbar and external faults. To validate the proposed scheme, numerous computer simulations have been carried out on an existing 220 kV Indian power generating station having different types of bays such as line, transformer, reactor & generator with generator transformer (GT). Various fault conditions (test data set of 23,760) have been simulating using the power system computer aided design (PSCAD)/ electromagnetic transient direct current (EMTDC) software package (Winnipeg, MB, Canada) by varying fault & system parameters. The proposed RVM based fault discrimination scheme is executed in MATLAB software (The Math- Works, Natick, Massachusetts, USA) by loading the simulation data. Comparative evaluation of the proposed RVM based scheme with the existing support vector machine (SVM) based scheme clearly indicates the superiority of the proposed scheme in terms of decision speed (faster than SVM based scheme) and classification accuracy (more than 99%). Moreover, it does not operate under different types of external faults and system disturbances. Subsequently, the proposed scheme remains stable during severe current transformer (CT) saturation condition.
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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