A New Algorithm for Busbar Fault Zone Identification Using Relevance Vector Machine
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".