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 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".