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AI-Based Intelligence Fault Diagnosis and Predictive Maintenance System for Smart Power System
Subject area: Science,Engineering and Technology · Area of research: Smart Power System
DOI: https://doi.org/10.64388/IREV9I11-1717363
Abstract
The integration of Artificial Intelligence (AI) into predictive maintenance for smart grid components has revolutionized the reliability and efficiency of modern power systems. This study explores advanced AI-based predictive maintenance models designed to anticipate failures and optimize maintenance schedules for key smart grid assets such as transformers, circuit breakers, and sensors. By leveraging machine learning algorithms and real-time data analytics, these models enable early fault detection, reduce downtime, and lower operational costs. The implementation of AI-driven predictive maintenance in smart grids supports enhanced grid stability, improved asset lifespan, and sustainable energy management. Challenges and future directions for AI applications in smart grid maintenance are also discussed to promote resilient and intelligent power networks.
Keywords
Artificial Intelligence, Predictive Maintenance, Smart Grid, Machine Learning, Fault Detection, Power Systems, Asset Management, Real-Time Analytics, Grid Stability, Energy Efficiency
How to cite this paper
@article{1717363,
author = {S. Nirmala Devi, Abinaya V, Divya R, Nithiya Shree P, Selvapriya S.},
title = {AI-Based Intelligence Fault Diagnosis and Predictive Maintenance System for Smart Power System},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {1518-1523},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717363.pdf},
abstract = {The integration of Artificial Intelligence (AI) into predictive maintenance for smart grid components has revolutionized the reliability and efficiency of modern power systems. This study explores advanced AI-based predictive maintenance models designed to anticipate failures and optimize maintenance schedules for key smart grid assets such as transformers, circuit breakers, and sensors. By leveraging machine learning algorithms and real-time data analytics, these models enable early fault detection, reduce downtime, and lower operational costs. The implementation of AI-driven predictive maintenance in smart grids supports enhanced grid stability, improved asset lifespan, and sustainable energy management. Challenges and future directions for AI applications in smart grid maintenance are also discussed to promote resilient and intelligent power networks.},
keywords = {Artificial Intelligence, Predictive Maintenance, Smart Grid, Machine Learning, Fault Detection, Power Systems, Asset Management, Real-Time Analytics, Grid Stability, Energy Efficiency},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717363}
}