Efficient Fault Detection in Renewable Energy Systems through IoT and Isolation Forest Algorithm

As renewable energy sources are becoming more integrated into the power grid, ensuring system reliability is crucial. A unique defect detection method for renewable energy systems leveraging the Internet of Things (IoT) and the Isolation Forest algorithm is presented in this research. The integration of IoT devices provides real-time monitoring and data collection from solar panels, wind turbines, and energy storage units. The machine learning Isolation Forest method effectively finds data abnormalities and defects. The Isolation Forest algorithm can correctly detect problems and abnormalities in renewable energy systems, enabling early maintenance and repair. This proactive defect detection technology improves renewable energy’s reliability and efficiency, minimizing downtime and power grid stability. IoT technology and the Isolation Forest algorithm improve problem detection and system performance and creating a more sustainable and cost-effective energy-producing infrastructure. It shows that advanced data analytics and machine learning may make renewable energy systems more reliable and appealing as clean energy sources.