Remote diagnostics and digital twins for offshore wind

Help implement smart, remote diagnostic tools for managing offshore wind assets for SSER, a leading developer and operator of renewable energy across the UK and Ireland.

Background

Scottish & Southern Electricity Renewables (SSER) is a leading developer and operator of renewable energy across the UK and Ireland, with a portfolio of around 4GW of onshore wind, offshore wind and hydro. SSER has the largest offshore wind development pipeline in the UK and Ireland at over 6GW and has an onshore wind pipeline across both markets in excess of 1GW.

Industries such as automotive and aerospace have paved the way for effective use of digital twins and AI-enabled modelling and in doing so have adapted their operating and maintenance model to allow more informed and progressive decision making.

The offshore wind sector is yet to widely utilise digital twin technology, however doing so offers opportunities to increase safety, reliability and optimal efficiency of turbines by enabling pre-emptive monitoring and maintenance.

SSER has significant individual system analysis data and modelling for their assets and now require a solution for integrated data management and an innovative digital approach for whole-system structural analysis and modelling as well as WTG major component diagnostics.

The challenge

The challenge is to use existing data streams to present asset information in an accessible format and provide actionable information for inspection and maintenance planning.

Solutions would ideally implement smart, remote diagnostic tools (e.g. digital twin technology) that aim to support a condition-based maintenance system’s (CMS) approach to managing offshore wind assets.

SSER wishes to incorporate its CMS and Supervisory Control and Data Acquisition (SCADA) data of all wind turbine components from the foundation up into a single tool.

This includes, but is not limited to:

  • Wind turbine components (drivetrain, blades and ancillary systems)
  • Electrical components (excluding transmission system)
  • Substructure components (including foundation, tower, transition piece)

Rewards and benefits 

Successful applicants will be given an opportunity to pitch to SSER representatives. Successful solutions may be trialled at SSER sites.

The package may also include:

  • Support from KTN
  • Technical support
  • Invitation to attend or present at KTN events
  • Support if any Innovate or similar competitions are relevant.

Functional requirements

The solution should deliver (and integrate) the following functionality for main component categories/systems: 

Substructure systems

  • Incorporate structural health monitoring of foundation, tower and transition piece structures.
  • Deliver performance predictions of substructures based on installed structural health monitoring system data inputs and known design parameters. This will act as the basis for asset life assessment and life extensions decision-making.
  • Incorporate inputs from manufacture and fabrication of structures to provide continual monitoring of known failure modes.

Wind turbine systems - drivetrain

  • Incorporate drivetrain monitoring system data e.g. vibration.
  • Ascertain for how long drivetrain components will operate safely, post any fault identification.
    - A major drivetrain component changeout campaign necessitates the need for a jack-up vessel, which is a key cost driver for operations.
    - SSER are moving to a ‘One jack-up vessel campaign per year’ strategy (to be carried out during summer months) so need to ensure, where possible, that components can operate until the next exchange window. (Note: this is a key objective)
  • Additional issue, to note: Generator wedge ejection failure prediction on 3.6MW scale turbines. Explore options using existing data streams and also recommendations for any sensors/systems which will help predict these failures.

Wind turbine systems – blades and rotor

  • Incorporate data from blade inspection campaigns and data from the wind farm SCADA system.
  • Ascertain for how long blades will operate safely, post any fault identification.

Wind turbine systems – Ancillary and electrical

  • Incorporate data from ancillary and electrical components e.g. converter systems. Examples include fans, delta modules and coolant systems (pressure loss via system leaks/increased production levels/accumulator failures etc.). 

SSER are happy to explore other components which have common failure modes or include data from other sources. For example, data from the nacelle-mounted anemometer may also be considered. 

Any solution should seek to provide, across all components, a prediction of component failure where possible, as well as any preventative maintenance recommendations based on existing SCADA data.

SSER is particularly interested in modelling the interaction between systems, for example the effect of the rotational parts on the structure.

SSER has significant data from deployed sensors with individual data streams, therefore further sensor implementation and data acquisition is not the focus of this challenge. A viable digital tool can be built without any additional data acquisition, however there may be opportunity for further data streams and sensors to be considered in the future.

This project has the ability to grow in scope with the integration of further data sources.

Technical requirements

Solution providers should seek to use known failure modes to determine typical failure patterns and trends. This should aim to identify failures before they happen, to allow a preventative approach instead of reactive. This should be based on data for SWT-3.6 (3.6MW) Siemens Gamesa Renewable Energy (SGRE) turbines, SG 8.0 (8MW) direct drive SGRE turbines and V164 (10MW) Vestas turbines, as well as any future models added to the SSER fleet.

Solutions should use existing drivetrain data streams such as Transmission Control Module (TCM) and SCADA, but also be able to incorporate new monitoring systems such as live oil monitoring to determine the health of major components such as gearbox, generator and main bearings.

Health should be determined by expected time to failure and useful component life on a continuous basis, using fault progression data. This should be based on known and emerging failure modes of major components for SWT-3.6 (3.6MW) SGRE turbines, SG 8.0 (8MW) direct drive SGRE turbines and V164 (10MW) Vestas turbines, as well as any future models added to the SSER fleet.

For SSER’s existing sites, Greater Gabbard (SGRE SWT-3.6) and Beatrice (SGRE SG 8.0), it has comparable SCADA across different turbine models. SSER would like a whole integrated system across all inputs.

Closing date

Launch of the Competition: 17/06/22
Deadline for applications: 02/08/22
Selection and notification of finalists: 30/08/22
Date of Pitch day: TBC 

Solutions should be capable of full implementation and operation within 2 years.

How to get involved

For more details about this challenge and to apply, visit the KTN Innovation Exchange website.