Digital Programs

Creating digital value for powerful customer experiences

Digital Programs

In SGRE we prioritize the experience of our customers and do our best to enhance it with every product we deliver. Digital Ventures Lab (DVL) is our digital transformation driver to integrate digitalization technologies into the value chain that bring customer experience to a new level, focusing on customization and optimization of our products from the very first touchpoint.

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Digital Programs

In close collaboration with our customers, DVL has defines digital programs comprising of corresponding digital solutions to boost efficiency in various business process phases.

Based on open innovation,  state-of-art dataset from satellites and technologies such as machine learning, the Digital Proposal Manager  provides tools and information to speed up as well as to enhance the analysis of project conditions to ensure a swift proposal process leading to most efficient solutions for our customers"

Why:

To offer customers most efficient solutions (cost/performance) for their wind farms and to speed up the process/throughput time of proposals. This includes the optimization of wind farm layouts and designs for onshore towers as well as for offshore substructures according to site-specific conditions.

What:

A set of solutions supporting our customers as well as our sales and engineering community during the proposal process. Tools are being developed for the site condition and suitability assessment along with design and manufacturing applications for towers and substructures that enable an optimization for the use in a particular windfarm (onshore/offshore).

How:

  • By enhancing the siting process in speed and accuracy by latest technologies and algorithm developed in collaboration with renowned research institutes.
  • By supporting the understanding of technical regulations for the planned project and its corresponding solutions.
  • By assisting sales/engineering teams in the identification of optimal solutions and enabling the automatization of the design process.
  • By improving the interface between customer and SGRE (ease-of-use and speed) for exchange of information.

Value Chain Tracing increases transparency and monitoring of the steps during manufacturing, transportation, installation and servicing of SGRE’s wind turbines by utilizing existing processes data and IoT sensors information. Over the lifetime of the product, this “turbine CV” records all relevant information on how the product has been built and maintained, therefore improving the predictive modelling of component life and maintenance optimization.

Why:

Improving speed and quality of manufacturing, transportation and installation by guiding and advisory systems.  Enables analytics to include lifetime and source information for better predictions.

What:

Enable tracing of key components and equipment during design, manufacturing, transportation, installation and service to provide up-to-date status and concluding decisions.

How:

  • Tracing of physical position of components and equipment during manufacturing and transportation.
  • Support manual process steps in blade manufacturing with guidance systems
  • Increased number of process parameters automatically recorded and analyzed for process/quality improvements.
  • Enhanced tracing of individual components incl. their manufacturing and maintenance history as used in individual turbines.

 

Creating and configuring models for individual components of a wind turbine during the manufacturing process will allow to build a virtual version or digital twin. Based on the data recorded during the actual wind turbine operation, it is possible to monitor and analyze the condition and performance for each of these components and take corresponding actions for identifying faults or maintenance needs and optimizing the service tasks and operation strategy.

Why:

Enable enhanced power sales as well as asset operation and maintenance strategies by an improved understanding of the actual condition and performance for components in a wind turbine at any time given the actual project condition this wind turbine has been exposed to so far.

What:

Build up and run individual simulation models (“digital twins”) mirroring the operational status and condition of turbine components based on manufacturing and/or operational data provided from their real counterparts in the field.

How:

  • Development of models for turbine key components to reflect condition and performance based on historic operational data.
  • Specification of site- or blade production.

Business analytics are applied to various areas of the business using state-of-the-art algorithms and statistical methods as well as state of the art visualization concepts.  Exploring business data, performing comprehensive analysis and visualizing the corresponding results, provide a solid base for an informed decision making built upon an improved understanding of business operations in combination with real-time information.

Why:

Support informed decisions for business operations based on an analysis as well as visualization of operational data.

What:

Set of dashboards, scorecards and further reporting tools providing improved insides into historic, current as well as expected performance and characteristics of business operations.

How:

  • Dashboards used to forecast and plan utilization of equipment and shopfloors.
  • Scorecards visualizing performance metrics of production lines, as used e.g. during daily stand-up meetings.
Core Technologies

The main technologies we use are the following:

 

Remote Sensing Data

Our remote sensing data for climate conditions, surface objects, and metocean parameters acquired via satellites and airborne measurements can complement corresponding site data as it is collected for an anticipated project via traditional measurement campaigns.

 

Machine Learning

In SGRE we apply machine learning across all areas of industry: from predicting long term wind conditions given short term wind measurements at a potential site, to prognostic analytics guiding preventive maintenance strategies at operating wind farms.

 

Edge Computing

Edge computing allows us to bring processed data to the place where it is needed instead of loading the data into a cloud, processing it there and then returning. It offers the opportunity to analyze data on-site almost in real time and, for example, to derive forecasts that significantly improve quality and benefits.

 

IoT

We use cutting edge  IoT solutions to connect a network of assets that contain embedded technology to sense and interact with our performance, maintenance and predictive systems.

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