In this study, the maximum power extraction condition for a direct-drive wave energy converter in irregular waves in the time domain was proven and described using the variational method. Thus, a real-time optimal power control law was proposed, which contained a noncausal part. To determine this law, a classical controller requires information about the future wave excitation force. The prediction of a wave excitation force is either too costly or less accurate. This study presents a novel optimal power control method based on a back propagation (BP) neural network without wave prediction. The network was used to learn the input−output mapping relationships of the noncausal part, and it used the history data of the wave excitation force as the input. The setting and training of the network models are discussed in detail. The history data of the wave excitation force were identified using a Kalman filter. The simulation and experiment demonstrated that the proposed method was valid, effective, and superior to some existing methods.

中文翻译：

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基于神经网络的不规则波直接驱动波能量转换器的功率控制方法

在这项研究中，在时域中不规则波中直接驱动波能量转换器的最大功率提取条件得到了证明，并使用变分方法进行了描述。因此，提出了包含非因果部分的实时最优功率控制律。为了确定该定律，经典控制器需要有关未来波浪激励力的信息。波浪激励力的预测要么太昂贵，要么不太准确。这项研究提出了一种新的基于反向传播（BP）神经网络的最优功率控制方法，而没有波预测。该网络用于学习非因果部分的输入-输出映射关系，并使用波浪激励力的历史数据作为输入。将详细讨论网络模型的设置和训练。使用卡尔曼滤波器来识别波浪激励力的历史数据。仿真和实验表明，该方法是有效，有效，优于现有方法的。