Build an AI Solar Fault Detection System with Raspberry Pi 5 - Identify Shading, Soiling, and Cell Degradation Instantly

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

Solar installations are booming across India, but invisible faults like partial shading, soiling, and early cell degradation silently slash energy output. This kit lets you build an edge-AI system that spots those faults in seconds, using a Raspberry Pi 5, INA226 power monitors, and a TensorFlow Lite classifier you train yourself. By tracing IV curves on two real 10W panels, the system learns to distinguish three common failure modes, giving you the same kind of insight used by professional solar diagnostics - without needing a lab full of expensive equipment.

What You'll Build

You will assemble a portable fault detection station that connects to two solar panels via four INA226 modules, captures high-resolution current-voltage sweeps, and streams them to a Pi 5. A TFLite model running on the Pi's CPU then classifies each IV curve as normal, shading-affected, soiled, or suffering from cell degradation. The result is displayed on an attached screen or logged for further analysis - a complete, ready-to-demonstrate project for your engineering portfolio.

What You'll Learn

• Tracing IV curves by sweeping load conditions and sampling current/voltage synchronously
• Interfacing INA226 power monitors with a Pi 5 over I2C and reading electrical parameters programmatically
• Training a TensorFlow Lite model to classify solar panel faults from real-world data
• Deploying a TFLite model on a Raspberry Pi 5 and integrating it with live sensor input for on-device inference

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

This kit is designed for CBSE Class 11-12 students pursuing AI or renewable energy modules, B.Tech ECE/EEE learners working on mini-projects or Smart India Hackathon entries, and researchers at NITs, IITs, and VIT who need a reproducible edge-AI testbed. It also fits perfectly into ATL Tinkering Lab curricula, offering a real-world application of sensors, embedded systems, and machine learning in a single project.

Built and Backed by Compoden

Every Compoden kit ships with an AI build companion trained on this exact project - accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.