We propose that governments ensure sufficient attention to variety and experimentation in innovation systems while maintaining a focus on rapid diffusion. ![]() A rapidly formed dominant design and quick diffusion are critical to ensuring countries meet their climate pledges, but may risk early lock-in if there is no room for experimentation. It concludes that well-positioned incumbents and a specific innovation system architecture have created this trend, a notion applicable to a broader socio-technical system context. It then proceeds to illustrate how The Netherlands has promoted and embedded a rapidly formed dominant design through an analysis of its offshore wind innovation system based on 31 interviews. This paper empirically demonstrates this reversed innovation trend and then proposes a new innovation dynamic founded in a new era of grand societal challenges. This trend reverses classic innovation pathways. Radical experimentation, normally expected at the beginning of technological development, only began to emerge after 20 years of diffusion. This is indeed the case in offshore wind, in which a specific institutional architecture has led to a rapidly formed dominant design that emerged early in the technology’s development. These new forms of innovation policy may seriously alter classic innovation dynamics. This new era requires a massive restructuring of societies, industries and consumption and will depend on, in part, new technologies and a high degree of coordination between the industry, civil society and government. Tests performed for a case study based a theoretical floating wave energy converter produced results in good agreement with reality.Ī new era of transformative and mission-oriented innovation policy has arisen due to the urgency of grand societal challenges, such as climate change. ![]() The overall operation assumptions and underlying operating principles of the statistical weather window model, maritime infrastructure selection algorithms, and cost modeling strategies are presented. A vessel charter rate modeling approach, based on an in-house vessel database and industry experience, is described in detail. A statistical weather window model was developed to estimate operation delays due to weather. Infrastructure selection logistic functions were developed to select vessels, ports, and equipment for specific projects. The present paper describes the methodology of a novel, opensource, logistic and marine operation planning tool, integrated within DTOceanPlus suite of design tools, and responsible for producing logistic solutions comprised of optimal selections of vessels, port terminals, equipment, as well as operation plans, for ocean energy projects. Planning the logistics of marine energy projects is a highly complex and intertwined process, and to date, limited advances have been made in the development of decision support tools suitable for ocean energy farm design. The logistics and marine operations required for installing and maintaining these systems are major cost drivers of marine renewable energy projects. Ocean energy is a relevant source of clean renewable energy, and as it is still facing challenges related to its above grid-parity costs, tariffs intended to support in a structured and coherent way are of great relevance and potential impact. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. ![]() This novel technique compared several models per turbine, each trained on a different number of months of data. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task.
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