Published: 25 September 2020

Dr Hong Yue is Reader at the Wind Energy and Control Centre, Department of Electronic & Electrical Engineering, University of Strathclyde

Bringing behavioural modelling to the nascent tidal energy sector is an exciting prospect for my team at the University of Strathclyde, providing a real-world platform for developing new technologies and involving researchers working across engineering departments. It’s also an opportunity to share knowledge with commercial partners from across Europe, including the project lead Nova Innovation, INNOSEA, Wood, Offshore Renewable Energy (ORE) Catapult, DNV GL, IDETA, France Energies Marines and ABB.

Our task on the project is to build a holistic model of tidal turbine behaviour and optimise the control systems being used in Nova Innovation’s Shetland Tidal Array using existing data from its operational turbines. By project end, we will have produced behavioural system models, predicted turbine loads and yields, optimised control system, and our work will fuel a turbine redesign that achieves a 17% reduction in the lifetime cost of energy (LCOE).

We completed the first stage of our work at the end of 2019, which was analysing site data and the existing controller used in Nova’s turbines. Looking at this historic data, we were able to chart controller performance and gain a detailed understanding of how Nova’s systems behave in the tidal streams.

Now, we have completed work on the second stage of our work package: developing an Initial Behavioural Model (IBM).


Before concentrating on advanced control and machine-learning concepts, we had to demonstrate the validity of the models being used to represent real-world tidal turbine systems. We also had to implement the existing operational control structures into turbine models, making a comparison of real-world and simulated data meaningful.

The Initial Behavioural Model (IBM) that we have produced incorporates the following elements:

Tidal flow field measurements. We have a backlog of data taken from Nova Innovation’s Shetland Tidal Array since the launch of our Horizon 2020 sister project EnFAIT (Enabling Future Arrays in Tidal) in 2017. This far-field hydrodynamic model includes tidal flow characteristics such as speeds, shear profiles and turbulence. A second model will be supplied from the Étel Estuary by our partners at INNOSEA later in the project.

Turbine models. We have created turbine model in hydro-elastic numerical simulators which are based on Blade Element Momentum (BEM) theory. Such programs allow for the efficient simulation of turbines in turbulent flows. The turbine itself is represented as an interconnected set of elastic and rigid bodies and these models include all the turbine’s main components such as: rotor, drivetrain, support structure and the turbine control strategy.

As part of the ELEMENT project the NOVA RE50 turbine will be tested on a floating platform. Therefore, it has also been necessary to model and understand the impact that moving from a fixed to floating support structure will have on tidal turbine behaviour and control requirements.

Structural Support Model. In order to understand loading, flow interactions and potential damage to the tidal turbine support structures, INNOSEA has developed a high-fidelity finite-element (FE) models in ANSYS software. This has allowed us to identify structural harmonic frequencies (important for control considerations as mentioned above); and to assess structural responses to the loading imparted to the structure by the flow and turbine interactions). We can now analyse the resulting loads and stresses throughout the support structure, as well as the characterising damage imparted by fatigue cycles and peak loads for this portion of the turbine.

Energy Yield. The behavioural model allows for energy yield of the modelled turbines to be predicted for the sites at which they will be located. This was done by taking simulated results under given flow conditions and combining them with distributions which represent the annual flow speeds and turbulence levels etc., sourced from the far-field hydrodynamics model.

Damage Model. During operation, fatigue and peak loads contribute to damage to a device and a large focus of ELEMENT is reducing this damage in order to improve reliability and extend operational lifetimes for these devices.

LCOE Model. The LCOE model developed in ELEMENT is based on that created during the EnFAIT project. We are using it to assess the overall impact of the control system on the overall goal of the ELEMENT project, this being to reduce overall LCOE by 17%. The LCOE model will provide a means to explore the trade-offs between improved control allowing for changes to yield, loads, CAPEX and OPEX in optimising lifetime costs.


Once we have validated our IBM against turbine data for each of the modelled turbines, we will begin work on an Advanced Behavioural Model. this will involve enhancing the IBM with state-of-art control concepts, in some cases taken from the wind energy industry and adapted to the tidal turbine case.

This work, in its turn, will fuel an Advanced Behavioural Controller (ABC) for installation in Nova’s turbines. We will develop state-of-the-art controllers for the turbine models investigated in this work. This will draw from years of experience working with wind turbines in a similar context. The synergies between wind turbine control and tidal turbine control will be utilised to ensure that the controller is at the cutting edge of technology.

Our journey culminates in a final period of analysis and refinements, where we will take the test results from the prototype used in the working turbines and ongoing research results, so we can refine our testing framework and validate our models for the project end.

For more information, read the Behavioural Model Specification Report in our Knowledge Centre.