A Conceptual Model Development for Evaluating AI-Driven Predictive Maintenance in SCADA-Integrated Smart Grid Systems
Bibliographic record
Abstract
This study develops a conceptual model for evaluating AI-driven predictive maintenance (PdM) in SCADA-integrated smart grid systems through a systematic review and meta-analysis conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. A structured search across six major databases, IEEE Xplore, Scopus, Web of Science, ScienceDirect, ACM Digital Library, and Google Scholar, covering the period from January 2013 to December 2024 yielded 4,847 initial records, of which 63 studies met the full inclusion criteria following rigorous dual-reviewer screening, quality appraisal using the Newcastle-Ottawa Scale, and risk-of-bias assessment; pooled effect sizes were subsequently computed using a random-effects model (DerSimonian-Laird estimator) to account for inter-study heterogeneity (I² = 78.4%), with subgroup analyses stratified by AI algorithm type, grid segment, and SCADA integration depth. Meta-analytic findings revealed a weighted mean fault detection accuracy of 94.3% (95% CI: 92.1%–96.5%), with deep learning architectures — particularly LSTM and hybrid CNN-LSTM models — significantly outperforming traditional machine learning approaches (SMD = 0.67; 95% CI: 0.48–0.86, p < 0.001), and real-time SCADA-integrated configurations demonstrating markedly greater reductions in unplanned outage rates (SMD = 0.81; 95% CI: 0.59–1.03) relative to offline processing alternatives, with no significant publication bias detected via Egger's test (p = 0.16). Grounded in this synthesized evidence, the study proposes the SCADA-AI Predictive Maintenance Evaluation Framework (SAPMEF), a five-dimensional conceptual model encompassing data acquisition and sensor integration, AI model architecture selection, real-time SCADA communication fidelity, maintenance decision optimization, and grid resilience metrics, offering researchers, grid operators, and policymakers a structured, evidence-based instrument for benchmarking and deploying intelligent PdM solutions, while identifying cybersecurity resilience, model interpretability, and cross-grid transferability as critical priorities for future research.
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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 | 0.112 | 0.177 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.020 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".