This paper explores how organisational culture shapes digitalisation in the wind energy sector. Based on a global survey and literature review, it finds that teams drive digital adoption more effectively than entire organisations, with companies ahead of universities. Size has little impact, but barriers such as limited budgets, vague strategies, and risk-averse leadership slow progress. The study offers practical recommendations to foster collaboration, innovation, and clear digital strategies.

New designs of large wind turbine blades have become increasingly flexible, and thus need cost-efficient monitoring solutions. Hence, we investigate if aerodynamic pressure measurements from a low-cost sensing system can be used to detect structural damage. Our research is based on a wind tunnel study, emulating a simplified wind turbine blade under various conditions. We show that using a convolutional neural network-based method, structural damage can indeed be detected and its severity rated.

This paper delves into the crucial task of transforming raw data into actionable knowledge which can be used by advanced artificial intelligence systems – a challenge that spans various domains, industries, and scientific fields amid their digital transformation journey. This article underscores the significance of cross-industry collaboration and learning, drawing insights from sectors leading in digitalisation, and provides strategic guidance for further development in this area.

Wind energy creates huge amounts of data, which can be used to improve plant design, raise efficiency, reduce operating costs, and ease integration. These all contribute to cheaper and more predictable energy from wind. But realising the value of data requires a digital transformation that brings grand challenges around data, culture, and coopetition. This paper describes how the wind energy industry could work with R&D organisations, funding agencies, and others to overcome them.

We further enhanced a knowledge base for choosing the most optimal wind resource assessment tool. For this, we compared different simulation tools for the Perdigão site in Portugal, in terms of accuracy and costs. In total five different simulation tools were compared. We found that with a high degree of automatisation and a high experience level of the modeller a cost effective and accurate prediction based on RANS could be achieved. LES simulations are still mainly reserved for academia.