Design of Transformer Health Monitoring System Using Tensor Flow Architecture

Ensuring the health of transformers is crucial for maintaining the reliability and longevity of electrical power distribution systems. This paper introduces an innovative approach that leverages edge computing architecture for real-time transformer health monitoring, with a particular focus on oil color detection, a critical indicator of transformer insulation and degradation. The system employs an ESP32 microcontroller to capture oil color images directly from the transformer's vicinity, enabling rapid data acquisition with minimal latency. Using TensorFlow, a deep learning framework, an efficient model is developed to analyze these images and accurately assess the condition of the transformer oil based on its color. This real-time analysis enables the early identification of potential problems, allowing for proactive maintenance and helping to avoid expensive breakdowns. By utilizing edge computing, the computational burden is shifted from centralized servers to edge devices, enhancing scalability, reducing network congestion, and ensuring prompt decision-making. This decentralized approach not only reduces the reliance on continuous internet connectivity but also provides robust performance even in remote locations. The proposed system deals a cost-effective, efficient, and scalable solution for transformer health monitoring, significantly contributing to enhanced reliability and reduced downtime in electrical power distribution networks. The integration of machine learning algorithms for image analysis underscores the potential of combining cutting-edge technologies to improve the monitoring and maintenance of critical infrastructure. This approach paves the way for future advancements in the field, highlighting the importance of real-time data processing and analysis in maintaining the integrity of power systems.