Risk-Based Optimization of Periodic Maintenance for Power Grid Equipment
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
Maintenance of electrical transmission equipment is key to ensure reliable power supply. Maintenance tasks are triggered by equipment failures (corrective maintenance), observed anomalies expected to lead to failures (condition-based maintenance), time (periodic preventive maintenance), or other factors. Periodic maintenance tasks are typically scheduled at fixed time intervals so degradation mechanisms can be detected and corrective measures applied as needed. Engineers generally choose these intervals based on their knowledge of failure mechanisms. In the context of electric power transmission systems, this should be a compromise considering at least equipment reliability, maintenance costs (for inspections, repairs, replacements and so forth), value of lost load (VoLL) and other risks inherent in power transmission (environmental, health, safety and so forth). Herein, an asset behaviour model, an event stochastic simulator, a power-flow simulator, and a risk model with a VoLL estimator are combined to quantify the total cost of periodic maintenance strategies. A blackbox optimization solver is then used to search for periodic maintenance strategies that minimize costs within specified constraints. As the event simulator uses a Monte Carlo method to output grid states where equipment fails according to preset statistical distributions, the problem is non-deterministic. However, timely and meaningful results can be obtained by adjusting the number of Monte Carlo cycles as well as the length of the timespan simulated and other parameters. This opens the way for multi-fidelity optimization, where these parameters are automatically adjusted during optimization. Ultimately, engineers may use this approach to select optimal periodic maintenance schedules that minimize the global risk for the system operator. The procedure is implemented with NOMAD (Nonlinear Optimization by Mesh Adaptive Direct Search), an open-source blackbox optimizer.
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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 | 0.000 | 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 it