RESP: A Real-Time Early Stage Prediction Mechanism for Cascading Failures in Smart Grid Systems
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Bibliographic record
Abstract
Cascading failures resulting from cyberattacks are one of the main concerns in smart grid systems. The use of machine learning (ML) algorithms has become more relevant in identifying and forecasting such cascading failures. In this article, we develop a real-time early stage mechanism (RESP) to predict cascading failures due to cyberattacks in smart grid systems using supervised ML algorithms. We use a realistic methodology to create a dataset to train the algorithms and predict the state of all components of the system after failure propagation. We utilize the extreme gradient boosting (XGBoost) algorithm and consider the features of both the power and communication networks to improve the failure prediction accuracy. We use the real-time digital simulator (RTDS) to simulate the power system and make the system more applicable. We evaluate the mechanism's effectiveness using the IEEE 14-bus system, which results in the XGBoost algorithm achieving a 96.25% prediction accuracy rate in random attacks. We show that RESP can accurately predict the state of a power system in the early stages of failure propagation using real-time data. Furthermore, we show that RESP can identify the initial failure locations, which can aid in further protection plans and decisions.
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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 | 0.001 | 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 | 0.001 | 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