A Case-Based Windows Graphic Package for the Education and Training of Power System Restoration
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| 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