Wind Turbine Fault Classification Using Support Vector Machines with Fuzzy Logic

Abstract

Rapid and accurate identification of faults on wind turbine blades is important to ensure the continued operation of wind power generation systems. This paper explores the implementation of Support Vector Machines (SVM) combined with fuzzy logic for image recognition and fault classification of wind turbine blades. We discuss the concept, ideas, and implementation of SVM for image recognition, and the final result is to implement these features into a system for detecting the various cracks and damages on the blades of wind turbines using a scale model. The final system will be tested on a scale model of a wind turbine blade. We will focus on what SVM is, what the crossover between SVM and fuzzy may look like, and how it will effectively be able to detect cracks in the blades of wind turbines.