An improved ANN based admittance relay using pre-processed inputs
Bibliographic record
Abstract
This paper addresses some of the issues associated with the conventional relay designs and presents an improved distance relaying pre-processing algorithm. Instantaneous current and voltage values obtained directly from the power system have been used to obtain the processed inputs. The presented algorithm has been combined with a neural network approach to eliminate the process of phasor estimation, which is usually used in most numerical relaying algorithms. The neural network has been trained to recognize the phase difference between the processed inputs, and therefore eliminates the need of calculating phasors. The processed inputs given to the neural network have a direct relationship with the outputs expected from a relay, which helps to use a data window lesser than one full cycle to accurately detect faults, making the algorithm faster than traditional designs. The neural network based relay has been trained using pure sinusoidal values and tested on a 17-bus power system simulated in PSCADtrade. The results show that the relay is able to detect faults in lesser time as compared to conventional relay algorithms while maintaining the integrity of relay boundaries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".