Condition Monitoring in Power Transformers Using IoT: A Model for Predictive Maintenance

Power transformers are critical components in electrical power systems, and their failure can lead to severe economic and operational consequences. Traditional maintenance strategies, such as time-based or reactive maintenance, are insufficient for preventing unexpected breakdowns. This paper presents a real-time condition monitoring model leveraging Internet of Things (IoT) technologies to predict transformer failures and optimize maintenance schedules. The proposed system integrates multi-sensor data acquisition, cloud-based analytics, and predictive algorithms to provide continuous monitoring of vital transformer parameters, including oil temperature, moisture content, vibration, and partial discharge. The model is validated using simulated and real-world case studies, demonstrating significant improvements in fault detection accuracy and maintenance efficiency.