Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions

Abstract

This article presents a comprehensive overview of the digital twin technology and its capability levels, with a specific focus on its applications in the wind energy industry. It consolidates the definitions of digital twin and its capability levels on a scale from 0-5; 0-standalone, 1-descriptive, 2-diagnostic, 3predictive, 4-prescriptive, 5-autonomous. It then, from an industrial perspective, identifies the current state of the art and research needs in the wind energy sector. It is concluded that the main challenges hindering the realization of highly capable digital twins fall into one of the four categories; standards-related, data-related, model-related, and industrial acceptance related. The article proposes approaches to the identified challenges from the perspective of research institutes and offers a set of recommendations for various stakeholders to facilitate the acceptance of the technology. The contribution of this article lies in its synthesis of the current state of knowledge and its identification of future research needs and challenges from an industry perspective, ultimately providing a roadmap for future research and development in the field of digital twin and its applications in the wind energy industry.

Digital Twins' Maturity: The Need for Interoperability

“…Attempts to create maturity levels to assess and guide the development of more sophisticated digital twinning solutions already exist for several domains, including built environment [17], manufacturing [18], ports [38], and (wind) energy systems [16], [47]. A comprehensive overview of the core contributions regarding DT maturity assessment, including a brief summary and a description of a DT with full maturity, and their respective domain context, is illustrated in Table II.…”

“…Although there already are papers providing characteristics designed to assess the capabilities and the maturity of existing and upcoming digital twinning solutions [15], [16], [17], these assessment frameworks are often strictly domain-specific and view their respective actual products, services or systems in isolation. Consequently, there is a need for domain-independent guidelines that contain a certain level of generality without becoming too vague.…”

Computationally Efficient Reduced Order Modeling of DFIG-Based Wind Turbines: A Novel Frequency-Weighted and Limited Model Reduction Approach With Error Bounds

“…Improving turbine technology is driven by the primary quest of efficiently generating electrical power using the wind's kinetic energy [1], [2]. Notable machines in this category include Permanent Magnet Synchronous Generators (PMSGs), Double-Fed Induction Generators (DFIGs), and Squirrel Cage Induction Generators (SCIGs) [3], [4].…”