Combining Asset Integrity Management and Resilience in Coping with Extreme Climate Events in Electrical Power Grids
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
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.
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 | 0.000 | 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 | 0.001 |
| 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