User Interface and Alerts
A user-friendly dashboard visualizes individual panel performance, fault classification, and confidence scores. Real-time alerts and actionable insights are provided to users for timely maintenance and optimization.
Model Updating and Continuous Learning
The system incorporates a feedback loop that uses confirmed fault events to retrain and improve model accuracy over time, enabling adaptive learning.
Data Acquisition
Real-time operational data including voltage, current, temperature, and irradiance is collected from individual solar panels using IoT-enabled sensors and microcontrollers.
Data Preprocessing
The acquired data undergoes cleaning, normalization, and feature extraction to ensure quality and relevance for AI analysis. Anomalies such as noise and missing values are filtered out using statistical and rule-based techniques.
AI Model Development
Machine learning and deep learning algorithms (e.g., Random Forest, CNN-LSTM) are trained on historical and real-time panel performance data to classify panel status and detect early signs of faults such as shading, hotspot formation, and degradation.
Fault Detection and Localization
The trained model is integrated into the mobile/web app to analyze incoming data in real-time. The app flags anomalies and localizes specific panels experiencing performance issues.