Accurate long-term prediction of wave excitation forces is critical for optimizing wave energy converters, enabling enhanced control, and improving energy absorption efficiency. Traditional prediction models often employ deterministic conservative approaches, overlooking uncertainties. This paper introduces a novel prediction method based on the probabilistic diffusion model, offering a more precise prediction while accounting for uncertainty. Notably, our approach goes beyond previous works by simultaneously achieving control objectives for maximizing energy absorption, contrasting with methodologies solely focused on prediction and control tasks. The proposed method utilizes long short-time memory to extract temporal information from historical wave excitation observation. The hidden features are then processed through a diffused probabilistic-based unit. Subsequently, a time-scale neural network is developed for wave excitation moment prediction, followed by the application of nonlinear model predictive control to maximize the energy absorption. The methodology is validated on the 1:20 Wavestar prototype devices through numerical simulations. Results indicate that the predicted excitation moment closely align with measured values. In cases 4, 5, 6, the proposed method yields a substantial improvement in maximum control energy absorption–22%, 16%, and 31%, respectively–compared to traditional prediction methods. The proposed approach not only achieves a more accurate wave excitation moment prediction with uncertainty consideration but also introduces advanced control strategies in a full-scale plant application. This work contributes significantly to the field by bridging the gap between precise prediction and effective control in wave energy conversion systems.
Read full abstract