Posters

Abstract

Wind turbine blade inspection and maintenance is a critical aspect of wind energy generation, as it directly impacts the efficiency and lifetime of wind turbines. For this workshop we present a proof of concept for a new approach to optimizing wind turbine blade inspection and maintenance actions using data mining and predictive analytics. Our approach leverages machine learning algorithms and statistical models to simulate future defects based on maintenance history, damage propagation models, repair cost and environmental conditions. By applying different inspection and maintenance strategies, we can estimate the budget, annual energy production (AEP), and health of the turbines over their lifetime. This enables optimization of the inspection and maintenance actions for minimum cost. We demonstrate the potential impact of our approach by optimizing the inspection and maintenance costs for an example wind farm. Our results show that optimizing inspection and maintenance strategies can significantly reduce whole life operation and maintenance costs. We will present the methods and results of the demonstration as well as discuss the limitations, and areas for future development. Our approach has the potential to contribute to the wind energy industry by providing an effective way to optimize inspection and maintenance strategies for wind turbines, reduce maintenance costs, and improve lifetime efficiency.