An Online Fault Categorization and Detection Monitoring System for Pv Sensors

Abstract

Photovoltaic (PV) systems are rapidly expanding as a sustainable energy source, but reliability issues can arise from faults in PV sensors. This research develops an intelligent online monitoring system to detect and categorize sensor faults in real-time. A comprehensive data-driven methodology is presented, encompassing data collection, preprocessing, feature engineering, fault categorization modeling, fault detection modeling, system architecture design, and performance evaluation. Sensor measurements from a PV installation are collected and preprocessed. Key features are extracted and used to train machine learning models for fault categorization and detection. A cloud-based architecture is implemented, enabling scalable real-time monitoring through APIs and micro services. Results demonstrate the system's effectiveness in classifying key PV sensor fault types with 90% accuracy and identifying anomalies with 88% precision and 91% recall. The low-latency architecture facilitates early diagnosis, allowing rapid notifications and mitigation. This research successfully demonstrates the value of integrating intelligent data analysis into PV monitoring, paving the way for higher solar system reliability.