The following section focuses on having reached a complete digitalization of an offshore wind farm, the evident changes needed to an offshore wind farm system reaching this state and how to manage the change. The figure below shows the connection between the adopted 5C architecture, the discussed technologies in their step-by-step and continuous development approach, and the smart connected system with foundation layer and the decision and visualization layer on top. Having reached the top level of the 5C architecture and having established a decision and visualization layer parallel to this by combining the set of technologies and capabilities, it is reasonable to expect the wind power asset management system to change together with the physical interface.
Figure 38 Connection between the systematic framework and the expanded smart connected system. Digitalization architecture adopted from Lee, et al. (Lee, et al., 2015)
9 Offshore wind farm system adaption to new technological climate
This chapter focus on the further development or adoption of the wind power asset management architecture presented in Section 1:2.4 It takes into consideration that especially through the IIoT and the cloud, the interfaces and the work-related input and output between the different modules is seen to change. The physical asset systems, the support systems and their functions and tasks is seen to inherently be the same as before.
With the major difference being the eased and increased connectivity between them sought to increase and distribute sharing and knowledge.
9.1 Industrial sharing and visualization platform
9.1.1 Break down the silos
The cloud and corresponding software will facilitate for the arrangement of data obtained throughout the offshore wind farm system. In figure 39, the analytics is set to be performed at the onshore asset operating & control system, this is just a suggestion as the analytics could also be performed in the cloud or partly at the edges of the system. The focus should be on this system´s ability to enable information-sharing and communication, and Figure 40 is explaining how the cloud could be segmented. The lower segment is where data from
Figure 39 Updated wind power asset management system, inspired by (El-Thalji & Liyanage, 2010)
different entities are gathered and the dotted line indicate the data repository, where data could exist in raw format or analysed data from either the edge or from an analysis centre.
If gathering data in this manner is successful, it could mean that one succeeds in breaking down the silos. This relies on a great confidence in connectivity as data and information would flow back and forth between the cloud and the modules. It also relies on that all data are accessible or as much as possible is made open for access.
9.1.2 Extract the value of accessible data and make it understandable
The business intelligence (BI) section work to make sense of the different data and include the applications, infrastructure and tools to generate information and knowledge. The BI is the area where data in different forms are given deeper understanding. One example could be that the prescriptive analysis performed in module 2 is considered in relation to vessel and crew status, other prescriptive analyses, lessons learned and so forth to support decision makers as much as possible. It is here where implemented AI could enable learning as more and more iterations are made and situations encountered, thus over time establishing greater confidence in proposed decisions. This could potentially lead to a state where tasks and decisions, up to a certain level, could be handed over to the software. This area however, where machines makes high level decisions potentially affecting large portions of the organization needs thinking through to find a suitable solution with potential barriers to not take any chances. Data virtualization are central in the BI context, where some of the capabilities of data virtualization are; the ability to perform data federation, deliver data as requested by users, data transformation, quality improvement and integration of data depending on the requirements (Techopedia, 2017). In addition to this, data virtualization provide the abstraction layer for the platform to hide complexity and simplify information access (Cisco, 2017).
Figure 40 Suggested segmentation of the Cloud system
9.1.3 Make the information and knowledge available to relevant persons and stakeholders
On top is the industrial sharing and visualization platform that is seen to operate as an information distribution solution. The data and the business intelligence are the underlying criteria for the sharing and visualization platform to be established. This platform can contain applications, dashboard, portal and tools to enable all relevant parties, throughout the value chain of the wind farm, access to information and knowledge from any location.
This platform is to be viewed as the decision layer above the smart connected system and parallel to the configuration and cognition level of the 5C architecture. Where outputs are easily obtainable, understandable and accurate, serving as the decision makers´, operators and technicians right hand and confidante. This relationship is visualized in the figure below.
The platform will be an industrial social collaborative solution where workers from different companies or segments could interact and potentially improve work-processes, not having to wait for a printed-out piece of paper for instance. An interactive industrial community that through the federated and analysed data and expert knowledge together will establish a more complete decision base for problem solving, a distributed knowledge system. The entire platform should be flexible and scalable, when an increasing number of farms are planned, installed and operated, they should be included in this solution. The same goes for different stakeholders throughout the value chain, with a limitation to access depending on the time of contribution to the project, following a hierarchy and so forth.
Figure 41 Connection between the cloud and the, decision and visualization layer
Following this should there also exist a solution for knowledge transfer and an overview of who knows what.
9.2 Physical interface
Given that a decision layer of this sort gets established it could also have implication on the physical interface diagram described in Section 1:2.4.1. The main interface still being remote control and communication, however, it will be relieved from several work tasks and processes due to the established autonomy amongst the assets in the different modules.
The diagram in Section 1:2.4.1 show that individual-assessed databases needs integration and responsible sharing, considering the described solution above, it is seen that the distributed knowledge system enabled through digital technologies could do just that.
Considering the physical interface diagram, it highlights the information of life-cycle processes and the input/output of sub-systems representing the information creator-user relationships. A distributed knowledge system, an industrial social collaboration platform would enable a different user interface when it comes to messaging, notification and work order systems and user interface in general. Given a more lenient sharing approach and open access solution amongst stakeholders, it would return a more complex physical interface, but where the information creator-user relationships are more flexible and the information more obtainable.