Contextual Approaches to Data-Driven Fault Detection in Solar Photovoltaic System

Renewable energy offers a sustainable solution to climate change and environmental degradation by reducing greenhouse gas emissions and pollution. Solar energy, in particular, is a promising source due to its global availability, environmentally friendly operation, and declining costs. However, faults in solar photovoltaic (PV) systems can diminish their efficiency and pose safety risks. Detecting and diagnosing these faults is crucial for optimizing performance, minimizing downtime, and ensuring safety in solar PV installations. Contextual data, widely used in fields like NLP, computer vision, and recommender systems, are not as prevalent in the domain of solar PV systems. This paper focuses on leveraging contextual information to enhance fault detection in PV systems using ML-based models. The objective is to explore methods for integrating contextual features into machine learning models for AC power prediction and fault detection in solar PV systems. Three strategies for handling contextual data are compared. The results showed that for AC power prediction, contextual expansion and contextual normalisation performed better than contextual model selection strategy.