A systematic review of resilience strategies with emphasis on the role of machine learning and quantitative assessment under adverse operating conditions
Bibliographic record
Abstract
This paper presents a simulation-based systematic review that quantitatively evaluates the effectiveness of various strategies in maintaining smart grid performance under different disturbances, using the IEEE 33-bus test system. The disturbances of islanding, line, generator, and renewable source outages, and their effect on the system resilience index were analyzed, showing a 3.46 % reduction compared to normal operation. To mitigate these impacts, several strategies were compared: the fixed Battery Energy Storage System (BESS) improved the resilience index by 2.78 %, the Mobile BESS (MBESS) achieved a 3.55 % enhancement, and demand response programs contributed only a 0.41 % enhancement. Additionally, in a line outage scenario between buses 6 and 26, the base case without BESS resulted in 0.460 MWh of Expected Energy not Served (EENS) and an equivalent outage duration of 0.12 h. In comparison, the integration of BESS reduced these values to 0.023 MWh and 0.01 h, highlighting the critical role of BESS in strengthening network resilience and ensuring service continuity. Additionally, this study reviews different Machine Learning (ML) methods across the four resilience phases and presents use cases for each phase. The study also presents recent DOE recommendations that prioritize low-cost, high-impact resilience measures. These include targeted investments in distributed generation, fuel security, robust distribution lines, smart monitoring, and vegetation management, demonstrating that grid resilience can be improved through practical, low-cost measures rather than major infrastructure projects. Consequently, this study offers practical insights for researchers on enhancing smart grid resilience.
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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 | 0.015 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".