IoT-Based Fault Diagnosis System for Solar and Wind Installations

This paper presents a novel Internet of Things (IoT) based fault diagnosis system for solar photovoltaic (PV) and wind power installations. The increasing deployment of renewable energy sources necessitates advanced monitoring and diagnostic solutions to ensure optimal performance and reduced downtime. The proposed system integrates multi-level sensing infrastructure, edge computing capabilities, cloud-based analytics, and machine learning algorithms to detect, identify, and predict faults in renewable energy systems. Experimental results demonstrate that the system achieves 96.7% detection accuracy for solar PV installations and 93.5% for wind turbines, with an average response time of 3.2 seconds. The implementation reduces maintenance costs by 29.4% and unplanned downtime by 37.8% compared to conventional approaches. This research contributes to advancing predictive maintenance strategies for renewable energy infrastructure, enhancing reliability, and optimizing operational efficiency.