A Comprehensive Study of Machine Learning Models and Computer Vision Techniques for Renewable Energy Forecasting

Abstract

This project aims to develop a method for wind turbine blade (WTB) inspection using machine learning and computer vision that would allow early detection and diagnosis of structural faults in WTBs, aiding in condition-based maintenance in the industry. At present, the industry relies on the use of manual inspections of blades for fault detection and diagnosis. The use of drones for inspection has been proven for bridges and dams and is in the process of being implemented in the OSW industry. However, current methods of inspection require huge volumes of data and labour-intensive pre-processing. This project aims to utilise machine learning methods, to reduce human input required in the detection and diagnosis of faults in WTBs. This will consist of developing a set of novel computer vision algorithms that can achieve high accuracies of fault detection and classification from limited datasets to introduce prior knowledge into the learning process.