Interactive Design and Application of Virtual Training System for Smart Substation Combining Configuration Algorithm and Digital Twin Technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Power system simulation training is one of the important means to improve the quality of operators and ensure the safe and effective operation of power systems.Research based on digital twin technology, combined with configuration algorithms to give the substation integration diagram model generation method, developed a smart substation virtual training system.The intelligent monitoring is studied, the digital twin-based substation output voltage anomaly detection method is designed using the tracking differentiator method, and finally the simulation test of the intelligent substation virtual training system is carried out.The analysis shows that the voltage anomaly detection method in this paper is highly accurate and can extract the voltage anomaly waveform, and the offset rate of its collected signal is significantly lower than that of the comparison method (11.58%~14.84%),which is only 0.54%.The training test of fast distance protection, differential protection and zero sequence protection verifies the feasibility and effectiveness of the virtual training system in practical application.The platform can effectively promote the reform of applied electric power practice courses and provide a backbone for the training of new power system talents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it