• Maithili Sunil Sawant

The increasing complexity of modern smart grids has created a growing need for reliable monitoring and maintenance of critical electrical assets. Power transformers, circuit breakers, switchgear, cables, and transmission equipment require effective maintenance strategies to ensure reliable and continuous power-system operation. Conventional corrective and time-based preventive maintenance approaches may result in unexpected failures, unnecessary maintenance activities, increased downtime, and higher maintenance costs. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) provide data-driven approaches for monitoring equipment condition and predicting potential failures. This paper presents a systematic review of Artificial Intelligence-based predictive maintenance techniques for smart grid assets. The review examines commonly used AI and ML algorithms, condition-monitoring parameters, fault-detection methods, health-index assessment, failure prediction, and Remaining Useful Life (RUL) estimation. Techniques including Artificial Neural Networks, Support Vector Machines, Random Forest, Decision Trees, Convolutional Neural Networks, and Long Short-Term Memory networks are discussed. The integration of sensors, SCADA, Internet of Things technologies, and data analytics with predictive maintenance is also examined. Furthermore, major challenges such as data quality, limited fault data, class imbalance, model explainability, cybersecurity, computational requirements, and real-time implementation are identified. Finally, research gaps and future directions involving explainable AI, digital twins, multimodal data fusion, edge AI, and federated learning are discussed. The review highlights the potential of AI-based predictive maintenance to support intelligent asset management and improve the reliability and availability of smart-grid infrastructure.

Artificial Intelligence, Machine Learning, Deep Learning, Predictive Maintenance, Smart Grid, Condition Monitoring, Fault Detection, Power Transformer, Remaining Useful Life, Asset Management.

"Artificial Intelligence-Based Predictive Maintenance for Smart Grid Assets: A Systematic Review of Algorithms, Applications, Challenges and Future Directions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 10, page no.a356-a367, October-2026, Available :http://www.jetir.org/papers/JETIR2610039.pdf

"Artificial Intelligence-Based Predictive Maintenance for Smart Grid Assets: A Systematic Review of Algorithms, Applications, Challenges and Future Directions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 10, page no. ppa356-a367, October-2026, Available at : http://www.jetir.org/papers/JETIR2610039.pdf