Data-Driven Resilience Enhancement for Power Distribution Systems Against Multishocks of Earthquakes
Bibliographic record
Abstract
Earthquakes, which consist of one intensive main shock and a series of aftershocks, can significantly damage power distribution systems (PDSs). In this article, a data-driven PDS resilience enhancement strategy is proposed against multishocks of earthquakes. In particular, the investment and prepositioning of mobile emergency generators (MEGs) is determined against multishocks of earthquakes. The reallocation of MEG and the repair scheduling are obtained considering aftershocks and postrestoration failures. A resistibility index (RI) is developed based on hierarchical hidden Markov model (HHMM) for stochastic resilience evaluation. The historical earthquake data are incorporated into the HHMM as observed information of multishocks of earthquakes. Based on the RI, the problems of prepositioning and reallocation of MEGs are formulated as mixed-integer programming problems. The problem of repair scheduling is formulated as an adaptive multiperiod two-stage stochastic programming problem, for which a revision period is introduced to allow the decisions to adapt to the uncertainties after the revision. To reduce the computational complexity, an iterative algorithm is presented based on linear programming relaxation. The strategy is verified via case studies on the IEEE 123-Node Test Feeder and historical earthquake data. It shows by considering RI, the resilience of restoration can be optimized against future shocks of earthquakes. Also, the overall consideration of MEG investment, prepositioning, reallocation, and repair scheduling against multishocks of earthquakes can achieve an improved restoration performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".