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.
Highlights
Among the different renewable sources, ocean waves represent one of the most powerful and in the last few decades have been widely investigated
The purpose of this work is to estimate the Wave Excitation Force (WEF) of a non-linear Wave Energy Converter (WEC) employing a Kalman Filter (KF) observer and a Neural Network (NN) model
The main aim is to assess the estimation performances in term of GoF for different sensor equipments in all the operating conditions of the Inertial Sea Wave Energy Converter (ISWEC) device
Summary
Among the different renewable sources, ocean waves represent one of the most powerful and in the last few decades have been widely investigated. The work of [1] reports a comprehensive review of the advances in Optimal Control, Model Predictive Control (MPC) and MPC-like techniques applied to the wave energy field. In [5] the wave force estimation and prediction problem for arrays of WECs is approached , comparing both global and independent estimators and forecasters. In the work of [6] two approaches are presented: the first approach is based on a KF coupled with a random-walk model of the WEF; the second performs a receding horizon—unknown input estimation. In [11] is studied the estimation of the wave elevation using the measurements from a nearby buoy employing a Non-linear AutoRegressive with eXogenous input network (NARX). Most of the work cited refer to single Degree of Freedom (DoF) WECs and few studies on non-linear multiple DoF systems have been conduced so far
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