DEVELOPMENT OF A CASE-BASED TRAINING SIMULATOR FOR POWER SYSTEM RESTORATION
Bibliographic record
Abstract
Due to the continued improvement of the reliability of power system components, electric power blackout has become a rare phenomenon in a modern power system. However, major faults and catastrophic events may lead to the collapse of an entire system. A power company would face huge financial and consequential losses as a result of a power blackout. It is essential to have a speedy restoration to minimize such losses. System operators gain little or no experience on restoration from their daily operation. System operators, therefore, should be trained to handle the complex operations involved in system restoration. \nA case-based expert system has been developed to train and familiarize system operators with various steps involved in a restoration process. \nA user friendly and straightforward case-based reasoning algorithm has been proposed to solve the problems of conventional rule-based algorithms. This expert system has been applied to restore a part of the Saskatchewan Power network from simulated blackout events. A user accessible, knowledge database has been developed based on previous experiences. Various mathematical analyses have been used to verify the risk of a proposed solution. A case adaptation process has been developed based on the system symmetry and its configuration. An object-oriented graphical user interface has been developed to communicate with the expert system. Various Windows° resources have been utilized to make the expert system user friendly. With this graphical user interface, a user can simulate a blackout event and the expert system then proposes a solution after consultation with the knowledge database. In its way to propose a solution the graphical user interface explains ongoing reasoning activities and creates an interactive environment. With a user accessible knowledge database, a user can apply his/her own knowledge and can ensure better participation in training sessions.
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.
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 | 0.000 |
| 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 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".