论文标题
使用机器学习方法预测波农场功率输出的估计器模型
Estimator Model for Prediction of Power Output of Wave Farms Using Machine Learning Methods
论文作者
论文摘要
波农产生的功率量取决于波能转换器（WEC）的布置以及通常的波浪条件。因此，在阵列中形成适当的WEC排列是最大化功率吸收的重要因素。从测试地点收集的数据用于设计一种神经模型，以预测波农的功率输出产生。本文着重于开发一种基于从澳大利亚南部海岸的四个真实波浪场景得出的数据集来预测波能的神经模型。应用的转换器模型是一种称为CETO的完全淹没的三线转换器。研究了WEC放置的精确分析，以揭示测试地点波农场产生的功率量。
The amount of power generated by a wave farm depends on the Wave Energy Converter (WEC) arrangement along with the usual wave conditions. Therefore, forming the appropriate arrangement of WECs in an array is an important factor in maximizing power absorption. Data collected from the test sites is used to design a neural model for predicting wave farm's power output generated. This paper focuses on developing a neural model for the prediction of wave energy based on the data set derived from the four real wave scenarios from the southern coast of Australia. The applied converter model is a fully submerged three-tether converter called CETO. A precise analysis of the WEC placement is investigated to reveal the amount of power generated by the wave farms on the test site.
