Combining Asset Integrity Management and Resilience in Coping with Extreme Climate Events in Electrical Power Grids
Bibliographic record
Abstract
Contemporary electrical utilities are part of critical national infrastructure and function in a complex business and operational environment. They are also complex by their internal structure, management and deployed modern technologies. As the complexity and interdependencies increase, electrical power grids face an increasing number of situations that create conditions for cascading, system-level failures caused by natural disasters, extreme weather phenomena and malicious human actions. Recent disturbances worldwide demonstrate that electrical utilities need to rethink their established approach and plan and act globally to deal with such situations, which are likely to keep recurring. New ways to cope with this new reality are needed. Combining the concepts of Asset Management (AM), Asset Integrity Management (AIM) and resilience may provide an efficient framework. To demonstrate the applicability of this approach, the current paper focuses on the evaluation, by a major North American electrical utility (Hydro-Québec), of the robustness and resilience of its transmission and distribution grids while facing a major ice storm in a large urban area. The analysis involved experts from numerous fields of expertise and collaborations with external stakeholders, such as municipality level public safety experts. The study outcomes served to increase the organizational safety awareness level in the enterprise and for the public. They also helped identify further potential improvements through optimal allocation of investment.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".