Optimal control strategies for wave energy converters (WECs) commonly require noncausal knowledge of the incident wave to maximize energy production. To enable these control methods, the prediction capabilities of two autoregressive (AR) models are evaluated. This work utilized autoregressive methods to predict the wave excitation force since they can be implemented in real time to adapt to changing conditions. The two models evaluated in this work are AR and AR with exogenous inputs (ARX) models. These models describe the wave propagation between two devices. To substantiate the validity of the predictions presented here, the wave excitation force was also estimated using an extended Kalman filter (EKF). The EKF incorporated nonlinear heave models of each body to determine the wave excitation force that was formulated as a harmonic disturbance to each system. The combination of the EKF and the AR models presents an opportunity to evaluate the prediction capabilities of what can be currently implemented on board WECs in real time; there is no need for offline training or postprocessed filtering of the wave. The ARX model incorporating excitation force data from other deployed bodies (the exogenous input) is shown to significantly improve the performance of the wave excitation force prediction. It is concluded that WECs in a wave farm may be able to improve their energy harvesting performance through enhancing their prediction capabilities by using wave estimation data gathered from other WECs. Experimental data from a two-body wave tank test is used for this work.

中文翻译：

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波浪能量转换器的激波力预测

波浪能转换器（WEC）的最佳控制策略通常需要入射波的非因果知识，以使能量产生最大化。为了启用这些控制方法，需要评估两个自回归（AR）模型的预测能力。这项工作利用自回归方法来预测波浪激励力，因为它们可以实时实现以适应变化的条件。在这项工作中评估的两个模型是AR和带有外部输入的AR（ARX）模型。这些模型描述了两个设备之间的波传播。为了证实此处提出的预测的有效性，还使用扩展卡尔曼滤波器（EKF）估算了波激发力。EKF合并了每个物体的非线性升沉模型，以确定波动激励力，该波动激励力被公式化为对每个系统的谐波干扰。EKF和AR模型的结合提供了一个机会，可以实时评估当前可在板载WEC上实现的预测能力。无需进行离线培训或对波进行后处理过滤。结合了来自其他已部署物体（外部输入）的激励力数据的ARX模型显示出可显着提高波浪激励力预测的性能。结论是，波浪场中的WEC可能能够通过使用从其他WEC收集的波浪估计数据来增强其预测能力，从而提高其能量收集性能。