Event Tree Reliability Analysis of Electrical Power Generation Network using Formal Techniques
Bibliographic record
Abstract
In recent years, there has been a significant proliferation in the use of Renewable Energy Sources (RES), such as wind/solar systems, for power generation. However, the main obstacle that these resources face is their intermittent nature, which greatly affects their ability to deliver constant power to the power network. This raises several reliabilityrelated concerns and existing sampling-based simulation tools, such as the Monte-Carlo approach, cannot guarantee absolute accuracy of the reliability analysis results due to their inherent incompleteness. In this paper, we propose to use formal techniques based on theorem proving to conduct the reliability analysis of electric grids as an accurate alternate approach. In particular, we use the HOL4 theorem prover, which is a computer-based mathematical reasoning tool. We demonstrate the effectiveness of our proposed approach by analyzing the reliability of the IEEE 39-bus power grid incorporating RES power plants and and also determine its reliability indices, such as System Average Interruption Frequency and Duration (SAZFZ and SAZDZ). To assess the accuracy of our proposed approach, we compare our results with the commercial reliability analysis tool Isograph and the MATLAB toolbox based on Monte-Carlo approach.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".