CorPower Ocean has secured financial backing from Vinnova, Sweden’s national innovation agency, to adopt cutting-edge artificial intelligence (AI) and test its ability to drive performance and control in wave energy technology.
The Wave energy AI-based Control Enhancement (WACE) Project is now underway and is set to run until November 2025. CorPower Ocean is being supported by project partner Norwegian University of Science and Technology (NTNU) – a world leader in education, research, and innovation for engineering systems in the marine environment.
It comes shortly after the Swedish tech developer secured €32 million Series B funding to support the commercial roll out of its technology taking wave energy towards a bankable mainstream energy source. The investment followed breakthrough results from CorPower Ocean’s commercial scale C4 device surviving the largest Atlantic storms on record while efficiently generating power in regular ocean conditions.
WACE Project Lead and CorPower Ocean Control Engineer, Gabriel Forstner, said wave energy converters (WECs) have gained widespread attention for decades, and AI-based methods have large potential to improve operating strategies.
“The optimal operation of wave energy converters is one of the key factors to lower the levelized cost of energy (LCOE) ultimately making it an attractive part of the future clean energy mix. The main goal of the WACE Project is to combine AI methods with optimal control to enhance our existing operating strategy and further improve the performance of our point absorber type wave energy converter.”
A model-based design framework will be used in the WACE project. After establishing requirements for the closed-loop control system, an AI-based optimal control strategy is being designed along with a hardware-in-the-loop (HIL) test set-up. CorPower Ocean anticipates that their WECs are operated in arrays or farms which will generate a vast amount of data. This project also aims for utilising the available data to enhance an existing model-predictive control strategy combining optimal model-based control with AI. This combination allows the exploration of more performant control schemes while keeping the WEC in its feasible operating region. The performance of the developed algorithm will be proven using HIL testing providing a realistic real-time environment while using the existing operating strategy as a baseline.
A key feature of CorPower Ocean’s technology is its ability to tune and detune according to ocean conditions, limiting the response to storms and amplifying the motion and power capture in normal waves. This has been clearly demonstrated during the C4’s operational period in Aguçadoura. The verification of the amplified motion and power capture by the phase control system is seen as a key outcome from the HiWave-5 demonstration project.
The tuning and detuning feature of CorPower Ocean’s WECs can be compared to wind turbines, where all modern turbines have a function to pitch the blades to alter the response to the wind conditions, limiting loads in storms while optimising yield in regular conditions. We believe that adding such a similar function to wave energy is key for making it a reliable and competitive source of clean energy.
Forstner added that numerical models are commonly utilised to develop an optimisation-based control strategy for WECs.
“The idea of this project is to utilise an existing control algorithm for WECs and combine it with AI-based methods to improve the performance of the overall closed-loop control scheme,” he concluded. “This approach is not limited to WECs and can be extended to improve existing control algorithms in a wide range of industrial applications.”
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