Stochastic Sequential Restoration for Resilient Cyber-Physical Power Distribution Systems
Bibliographic record
Abstract
Modern power systems are undergoing a paradigm shift from traditional grids towards smart grids. It fundamentally changes traditional power systems into complex cyber-physical systems. On the other hand, new challenges arise in terms of grid resilience, because natural disasters can cause damages to both cyber and physical systems. In this article, we propose a stochastic sequential restoration scheme for cyber-physical power distribution systems considering resilience. The sequential restoration problem is formulated as an uncertain Markov decision process (UMDP) with hurricanes incorporated as natural disasters. Different wind velocities and directions are considered as hurricane scenarios, which are used to obtain the fragility of distribution lines. The fragility functions are further used for the derivation of uncertain state transition functions of the UMDP. The minimax regret optimization considering the sample weights of UMDP is presented. The robust sequential actions are determined, such that the loads can be restored in a timely manner. To improve computational efficiency, a minimax regret policy iteration algorithm is presented based on the regret Bellman equation. Case studies are conducted based on the IEEE 123-Node Test Feeder and historical data of Hurricane Bonnie to demonstrate the effectiveness of the proposed scheme.
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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".