Control Co-Design of Power Take-off Systems for Wave Energy Converters using WecOptTool

• Published in: IEEE transactions on sustainable energy Vol. 14; no. 4; pp. 1 - 11
• Main Authors: Strofer, Carlos A. Michelen, Gaebele, Daniel T., Coe, Ryan G., Bacelli, Giorgio
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Co-design; Control systems design; Controllers; Critical components; Design optimization; Electric power; Energy; Force; Frequency-domain analysis; Generators; Impedance; Mathematical models; optimal control; Optimization; power take-off (PTO); Powertrain; Source code; Takeoff; Wave energy; Wave energy conversion; Wave energy converter (WEC); Wave power
• ISSN: 1949-3029; 1949-3037
• DOI: 10.1109/TSTE.2023.3272868

Improved power take-off (PTO) controller design for wave energy converters is considered a critical component for reducing the cost of energy production. However, the device and control design process often remains sequential, with the space of possible final designs largely reduced before the controller has been considered. Control co-design, whereby the device and control design are considered concurrently, has resulted in improved designs in many industries, but remains rare in the wave energy community. In this paper we demonstrate the use of a new open-source code, WecOptTool , for control co-design of wave energy converters, with the aim to make the co-design approach more accessible and accelerate its adoption. Additionally, we highlight the importance of designing a wave energy converter to maximize electrical power, rather than mechanical power, and demonstrate the co-design process while modeling the PTO's components (i.e., drive-train and generator, and their dynamics). We also consider the design and optimization of causal fixed-structure controllers. The demonstration presented here considers the PTO design problem and finds the optimal PTO drive-train that maximizes annual electrical power production. The results show a 22.0% improvement in the optimal controller and drive-train co-design over the optimal controller for the nominal, as built, device design.