FAULT PREDICTION IN RENEWABLE ENERGY SYSTEMS USING ARTIFICIAL INTELLIGENCE TECHNIQUES

Abstract

Renewable energy systems such as wind turbines and solar photovoltaic (PV) plants are increasingly critical to the global power grid, yet unplanned faults in inverters, blades, sensors, and grid-connection equipment cause significant downtime, revenue loss, and safety risk. This paper presents an Artificial Intelligence-driven framework for early fault prediction in hybrid solar–wind renewable energy systems using multimodal sensor fusion and physics-informed self-supervised learning. The proposed system fuses SCADA electrical readings, thermal imaging, and acoustic/vibration signals through a transformer-based architecture, and is pretrained on abundant unlabeled normal-operation data using physical degradation models (thermal fatigue and blade-stress equations) as auxiliary supervision, reducing dependence on rare labeled fault samples. An explainable early-warning module highlights the sensor modality and time window responsible for each prediction, along with a confidence score, to support actionable maintenance decisions. Experimental evaluation shows that the proposed fusion model achieves higher fault prediction accuracy than single-modality baselines and provides substantially longer early-warning lead times, particularly for gradually developing faults such as blade and gearbox degradation. The framework further demonstrates promising cross-technology generalization when tested on an unseen battery-storage asset type. This work contributes a practical, explainable, and data-efficient approach to predictive maintenance for renewable energy infrastructure.

Keywords