Reduction of the Non-Causal Horizon of the Optimal Wave Energy Converter Control
Volume 9A: Ocean Renewable Energy
Ocean waves provide a promising and abundant renewable energy resource. One reason wave energy technology is still not mature enough for commercialization is the high unit cost of generated electricity. This needs to be improved by a combination of device and associated controller design. A multi-float and multi-mode-motion WEC (M-WEC) enables much higher energy conversion compared with a single-float, single-mode WEC (S-WEC); however, the added complexity in dynamics of a M-WEC makes the
... WEC makes the corresponding controller design more challenging. While the majority of current WEC control research has been based on the control of S-WECs it has shown that control can significantly improve energy conversion. This paper aims to design a linear non-causal optimal controller for a M-WEC to demonstrate that this improvement also applies to more complex WEC systems. We choose a multibody attenuator type M-WEC called M4 as a case study for which the desirable feature of predominantly linear dynamics has been demonstrated. This means that a linear controller can be designed based on a linear hydrodynamic model without introducing an intractable computational burden for real-time controller implementation. Numerical results show that the linear non-causal optimal controller can significantly improve the power capture of M4 over a broad range of peak spectral wave periods by 40% to 100%. the School of MACE. Of relevance to this paper he has been developing wave energy devices for a decade through experiments and linear wave/body modelling resulting in the patented multi-float modular system M4. Guang Li (M'09) received his Ph.D. degree in Electrical and Electronics Engineering, specialized in control systems, from the University of Manchester, in 2007. He is currently a Senior Lecturer in dynamics modelling and control in Queen Mary University of London, UK. His current research interests include constrained optimal control, model predictive control, adaptive robust control and control applications including renewable energies and energy storage, etc.