An Early Stage Failure Prediction Mechanism in Smart Grid Networks
Bibliographic record
Abstract
ABSTRACT Smart grid systems, as modern cyber‐physical systems (CPS), introduce new interdependencies between power and communication components that can create new security challenges. One potential challenge that may arise is cascading failures resulting from cyber‐attacks or the failure of a component that needs to be detected in a timely manner. In this paper, we propose a novel early‐stage failure prediction (ESFP) mechanism that applies machine learning (ML) algorithms to enhance the security of smart grid systems. We use a realistic model to generate a dataset for training ML algorithms and develop a mechanism to predict the state of a system's components in the early stages before failures propagate in the system. ESFP can predict the final state of each power system component with respect to its initial failures. We apply the extreme gradient boosting (XGBoost) algorithm and examine the features of both the communication and power networks that provide high accuracy in predicting failures. We develop a new data generation procedure to construct a dataset containing electrical and network features and characteristics for training ML algorithms. ESFP also identifies the location of the initial failures as this allows for further protection plans and decisions. We evaluate the effectiveness of the proposed mechanism through an analysis conducted on an IEEE 118‐bus system. The proposed mechanism achieves 99.4% prediction accuracy in random attacks using the XGBoost algorithm. We also improve the time of the XGBoost algorithm by 75% by combining an unsupervised ML algorithm with this algorithm.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".