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jan 2023

jun 2027


Artificial intelligence for enhanced hydraulic turbine lifetime

Recent advancements in artificial intelligence and machine learning enables high-dimensional controlling and decision-making. In this project, state-of-the-art artificial intelligence will be developed to detect and control undesirable and damaging flow-induced oscillations to enhance turbine lifetime. A well-developed and trained model can not only detect the presence of damaging flow structures, but it can also take optimal decisions to reduce and control such structures.

Presently, the inevitable intermittency of electrical energy resources such as solar and wind power is compensated through hydropower systems. Meaning that hydraulic turbines are not necessarily working at the steady Best Efficiency Point (BEP) condition anymore as they are used in different off-design and transient operating sequences to stabilize the electrical grid. Such operations cause flow instabilities with pressure fluctuations, load variations, and cavitation, which may deteriorate the machine and reduce its efficiency leading to entirely different engineering requirements. Thereby, a sustainable energy production system cannot be achieved unless these damaging effects are mitigated, and the hydraulic turbines are adapted to new transient operations

The main aim of this project is to employ and further develop artificial intelligence state-of-the-art to efficiently and robustly detect, control, and mitigate flow-induced oscillations during off-design and transient operation of hydraulic turbines, for enhanced turbine lifetime. To reach this aim, deep neural networks will be explored through reinforcement learning to perform optimal decision-making for hydro turbines. It is also investigated how Physics-Informed Neural Networks can be used to reduce the time to get accurate numerical results.


Håkan Nilsson

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