EdgeGuard: AI-Driven Predictive Maintenance for Power Transformers
29 July 2026 • Admin
Power transformers are among the most critical assets in electrical distribution infrastructure. Their unexpected failure can result in power outages, safety hazards, equipment damage, expensive repairs, and long service interruptions. Traditional transformer maintenance practices often rely on periodic manual inspection, offline testing, or run-to-failure maintenance. These methods are expensive, slow, labor-intensive, and unable to detect rapidly developing faults in real time.
EdgeGuard is an AI-driven, edge-computing predictive maintenance system designed to continuously monitor transformer health and forecast failures before catastrophic damage occurs. The system acts as a retrofittable “Digital Doctor” for distribution transformers by combining low-cost industrial sensors, an ESP32 microcontroller, local intelligence, machine learning-based risk prediction, autonomous relay control, and a real-time web dashboard.
The proposed system monitors six major transformer health indicators: temperature, humidity, vibration, oil level, current, and voltage. These signals are normalized and processed through a Multi-Layer Perceptron neural network to classify transformer condition and estimate failure risk. If the predicted risk crosses a critical threshold of 80%, EdgeGuard automatically triggers a relay through GPIO 26 to isolate the transformer from the electrical network. The system also supports secure remote control, dashboard monitoring, API-key-based hardware authentication, JWT-based user access, WebSocket live updates, and automatic live-hardware detection.
With an estimated deployment cost of approximately ₹3,850, EdgeGuard offers a low-cost alternative to conventional transformer monitoring systems. Its cloud-independent operation and edge-based decision-making make it especially useful for rural and semi-urban distribution grids where connectivity and maintenance resources are limited.
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