A Case-Based Windows Graphic Package for the Education and Training of Power System Restoration
Bibliographic record
Abstract
This paper presents a case-based Windows graphic package developed by the authors for the education and training ofpower system restoration. In order to overcome the inherent complexities associated with a restoration switching sequence, a case-based reasoning (CBR) algorithm has been developed. An object-oriented Windows-based graphical user interface (GUI) has been developed to communicate with the expert system and to visualize outputs. Simulation results on a part of the Saskatchewan Power (SaskPower) network have been presented in this paper. The restoration practices of the SaskPower network have been collected and stored in a knowledge database called case library. The power flow and the implementation risk analysis tools have been incorporated into the expert system. A risk analysis tool has been used to determine the implementation risk due to the imperfect switching actions. Using the GUI, a user can simulate a blackout event on the system under study. The expert system proposes a restoration proposal after reasoning with the past solutions available in the case library. Using the power flow analysis tool, the user can check the system scenarios after the restoration proposal is implemented.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.175 | 0.034 |
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".