Real-Time Wave Excitation Forces Estimation: An Application on the ISWEC Device

Abstract

Optimal control strategies represent a widespread solution to increase the extracted energy of a Wave Energy Converter (WEC). The aim is to bring the WEC into resonance enhancing the produced power without compromising its reliability and durability. Most of the control algorithms proposed in literature require for the knowledge of the Wave Excitation Force (WEF) generated from the incoming wave field. In practice, WEFs are unknown, and an estimate must be used. This paper investigates the WEF estimation of a non-linear WEC. A model-based and a model-free approach are proposed. First, a Kalman Filter (KF) is implemented considering the WEC linear model and the WEF modelled as an unknown state to be estimated. Second, a feedforward Neural Network (NN) is applied to map the WEC dynamics to the WEF by training the network through a supervised learning algorithm. Both methods are tested for a wide range of irregular sea-states showing promising results in terms of estimation accuracy. Sensitivity and robustness analyses are performed to investigate the estimation error in presence of un-modelled phenomena, model errors and measurement noise.

Comparison of the ERA5 Wave Forecasting Dataset Against Buoy Record

“…Since synthetic wave parameters have many uses, depending on the objectives, it is possible to determine from which source to acquire them. Indeed, in the emergence of renewable energy from waves, a detailed assessment of sea states is fundamental to designing and optimizing wave energy converter [31][32][33] of offshore wind turbines [34]. This activity generally involves installing recording equipment at sea, and economic efforts must be made.…”

A model predictive control (MPC)-integrated multiphase immersed boundary (IB) framework for simulating wave energy converters (WECs)

“…9.4 describes the capability of MPC to adapt to changing sea states in which different AR models are used for different sea states. Considering the importance of wave excitation force prediction, other methods of prediction are also described in the literature, including the recursive least squares filter [34], the Kalman and extended Kalman filters [35,36,37,38,39], and neural networks [35,38,40]. In practice, some of these techniques may be easier to implement than AR.…”

Combining pendulum and gyroscopic effects to step-up wave energy extraction in all degrees of freedom

“…All of the previous techniques are applicable to arbitrary WEC concepts; however, more fundamental routs to increase energy efficiency is to operate on the underlying conversion principle [19]. A novel device is indeed proposed in this paper and in a recent patent, based on the idea to notionally merge two already successful devices, both using inertial coupling between the motion of a floating hull and the inner electromechanical system: the ISWEC [9] (Inertial Sea Wave Energy Converter) and the PeWEC [20] (Pendulum Wave Energy Converter). Both technologies perform best when the incoming wave direction is along the hull main axis, and/or the spreading factor of the wave is infinite (i.e.…”

“…Increasing WECs efficiency is a complex and multidisciplinary task, relying on a variety of energy-maximisation techniques, e.g. via model-predictive control [6], extremum-seeking [7], or time-varying control [8], time-effective estimation [9] and forecasting [10] algorithms, data-driven improved accuracy of numerical models of fluid-structure interactions [11] by means of the identification of grey-parameters in low-complexity models [12], mid-fidelity partially-nonlinear models [13], or direct implementation of fully-nonlinear Navier-Stokes fluid-dynamics equations [14]. Nevertheless, such models should preserve the inherent passive characteristics of the system [15], while remaining able to articulate typical instabilities of floating structures, e.g.…”