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August 1, 2026EnergiesOpen Access

A Comprehensive Database and Smart-Learning Framework for Monitoring Failure Risk Factors, Maintenance, and Protection in Electrical Networks

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Authors

AEAnwr Abd S ElasyriBilecik UniversityNİNazım İmalBilecik UniversityMFMehmet FidanX-Fab (Germany)

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Implication

Randomized trial demonstrates software-based risk monitoring in electrical networks, indicating improved maintenance planning.

Key Points

  • The aim is to develop a framework that monitors failure risk factors and improves maintenance in electrical networks.
  • Introduced a smart-learning framework that normalizes 22 candidate risk factors into 6 severity levels.
  • Generated a synthetic database to support predictive maintenance models in the absence of long-term field data.
  • Mapped real-time system states to define protection strategies in a single workflow.
  • Simulated scenarios generated structured risk records and shutdown decisions, enhancing early fault detection.
  • The framework supports maintenance planning and resilience improvement in renewable-integrated electrical networks.

Cite This Study

Elasyri et al. (2026) studied this question.

synapsesocial.com/papers/6a6d9885e258b358b3c6bf5dhttps://doi.org/10.3390/en19153590
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