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IOT-Enabled Predictive Maintenance Framework for Intelligent Electrical Power Systems
1, 2 UG Scholar, Department of Electrical and Electronics Engineering, Holy Mary Institute of Technology and Science, Hyderabad, Telangana, India. 3 Assistant Professor, Department of Electrical and Electronics Engineering, Holy Mary Institute of Technology and Science, Hyderabad, Telangana, India.
Published Online: May-August 2025
Pages: 07-10
The increasing complexity of modern electrical power systems has created a growing need for advanced maintenance strategies that ensure operational reliability, reduce downtime, and optimize asset utilization. Traditional maintenance approaches, including corrective and time-based preventive maintenance, often result in unexpected equipment failures, increased operational costs, and inefficient resource allocation. The emergence of the Internet of Things (IoT) has enabled a new generation of predictive maintenance solutions that utilize real-time monitoring, intelligent analytics, and data-driven decision-making. This research proposes an IoT-enabled predictive maintenance framework for intelligent electrical power systems. The framework integrates smart sensors, wireless communication technologies, cloud computing platforms, and machine learning algorithms to continuously monitor the health status of critical power system components such as transformers, circuit breakers, transmission lines, and substations. Real-time operational data including temperature, voltage, current, vibration, and insulation parameters are collected and analyzed to detect anomalies and predict potential failures before they occur. The proposed framework employs predictive analytics to estimate equipment health indices and remaining useful life, enabling proactive maintenance scheduling. Simulation-based performance evaluation demonstrates improved fault detection accuracy, reduced maintenance costs, enhanced equipment availability, and increased system reliability. The findings indicate that IoT-enabled predictive maintenance can significantly improve the operational efficiency of modern power systems while supporting the transition toward intelligent and autonomous grid infrastructures.
