Resilient Control Of A Wave Energy Converter under PTO Fault Conditions

• Published in: IFAC-PapersOnLine Vol. 55; no. 27; pp. 144 - 149
• Main Authors: Zadeh, Leila Ghorban, Brekken, Ted K.A., Fern, Alan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2022
• Subjects: Actor-Critic; Bayesian Optimization; PTO Fault; Reinforcement Learning; Wave Energy Converter; Bayesian Optimization; Reinforcement Learning; PTO Fault; Wave Energy Converter; Actor-Critic
• ISSN: 2405-8963; 2405-8963
• DOI: 10.1016/j.ifacol.2022.10.502

Due to ocean wave resource characteristics, wave energy converter (WEC) components are required to bear peak loads (i.e., torques, forces, and powers) that can cause component degradation and breakdown. In addition, any failure of a switching element (MOSFET or IGBT) in the power electronics drive for the Power Take-Off (PTO), or a malfunction of a pump or hose in the hydraulics can cause a PTO failure. In this study, a wave energy converter with two PTOs is designed to simulate the condition where one of the PTOs may fail to operate. A Reinforcement Learning (RL) based control strategy is proposed to deal with PTO failures since they are model-free algorithms and can adapt their policy to a changing environment. Results prove the adaptability of the proposed control model to the condition where one PTO fails to generate power. In winter sea conditions, before the fault, the WEC with two PTOs generates 255.1 kW and at the time of 9.9 104 s, the PTO fault happens and the mean power drops to 223.4 kW. After fault, the RL-based control retrains its policy and generates 247.3 kW