Wave Prediction Error Compensation and PTO Optimization Control Method for Improving the WEC Power Quality
Year: 2025
Type: article
Abstract: Reliable wave prediction plays a significant role in wave energy converter (WEC) research, but there are still prediction errors that would increase the uncertainty for the power grid and reduce the power quality. The efficiency and stability of the power take-off (PTO) system are also important research topics in WEC applications. In order to solve the above-mentioned problems, this paper presents a model predictive control (MPC) method composed of a prediction error compensation controller and a PTO optimization controller. This work aims to address the limitations of existing wave prediction methods and improve the efficiency and stability of hydraulic PTO systems in WECs. By controlling the charging and discharging of the accumulator, the power quality is enhanced by reducing grid frequency fluctuations and voltage flicker through prediction error compensation. In addition, an efficient and stable hydraulic PTO system can be obtained by keeping the operation pressure of the hydraulic motor at the optimal range. Thus, smoother power output minimizes grid-balancing penalties and storage wear, and stable hydraulic pressure extends PTO component lifespan. Finally, comparative numerical simulation studies are provided to show the efficacy of the proposed method. The results validate that the dual-controller MPC framework reduces power deviations by 74.3% and increases average power generation by 31% compared to the traditional method.
Source: Energies
Authors: Tianlong Lan, Jiarui Wang, Luliang He, Peng Qian, Dahai Zhang, Bo Feng
Institutions: Zhejiang University, Hangzhou Institute of Applied Acoustics
Language: English
Topic: Wave and Wind Energy Systems
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Awards: G1021565360, G1272529525, G2258812103, G270900048, G3281580868, G3296800250, G3550169740, G368710527, G3800397167, G3878656429, G4690921539, G4694520040, G4848328898, G5513112664, G5588239471, G6506193402, G6829103296, G6890174078, G7342338325, G7391348671, G7892571461, G8288823733, G8726667624, G8817353957
OpenAlex ID: W4412710974
