AI-Driven Fault Detection and Maintenance Optimization in Photovoltaic Systems: A Comprehensive Comparative Analysis of Machine Learning and Deep Learning Approaches

Abstract

The global transition to renewable energy sources necessitates the presence of strong monitoring and maintenance systems of photovoltaic (PV) systems. The traditional fault detection tools like manual inspection and sensor are time consuming and also costly. This paper reports a thorough research of artificial intelligence (AI) and machine learning (ML) algorithms in PV system fault detection and diagnosis. We demonstrate that fault detection accuracy is more than 98 percentage when sophisticated computational techniques are applied in the instance of systematic evaluation of hybrid AI designs, ensemble learning strategies, and deep learning approaches. We are going to examine Realtime monitoring systems, thermal imaging solutions, Internet of Things (IoT) solutions, and predictive maintenance solutions. The tree-based ensemble method of real-time fault detection and the degradation pattern recognition techniques of recurrent neural networks of our proposed dual-mechanism model. The findings indicate that the smart surveillance systems significantly affect the maintenance cost reduction, the enhancement of the system reliability, and the maximization of energy production efficiency. The research can be applied to enhance the establishment of sustainable energy infrastructure based on the evidence-based recommendations on the deployment of AI-based approaches in the large-scale solar facilities.