A REAL-TIME EMBEDDED FAULT DIAGNOSIS SYSTEMFOR PHOTOVOLTAIC MODULE MONITORING

Abstract

The advanced integrated technology demonstrated herein enhances the reliability and efficiency of solar energy systems by real-time identification of photovoltaic (PV) module faults. The system employs sophisticated machine learning algorithms and data processing methods to discover, classify, and anticipate problems such as discoloration, degeneration, and hotspots. The proposed method yields diminished maintenance expenses, fewer power interruptions, and enhanced power generation efficiency. The system has undergone experimental validation, confirming its accuracy and efficacy in actual solar setups, thereby establishing it as a significant asset for renewable energy management.