不规则波浪中PA-WEC发电量最大化的AI预测与控制力确定的基础研究

M. Murai, Shotaro Sakamoto
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引用次数: 2

摘要

海洋可再生能源有望成为化石燃料的替代能源。波浪能是海洋可再生能源之一。本文的主题是利用点吸收波能转换器(PA-WEC)在不规则波中最大限度地发电。在前人关于发电最大化的研究中,给出了由规则波分量叠加定义的不规则波中时域控制力的理论解。也就是说,如果PA-WEC可以同时将不规则入射波理解为多个特定的规则波分量,就可以确定发电量最大化的控制力。另一方面,安装在海洋中的PA-WEC很难同时准确地预测即将到来的不规则波的分量。近年来,人工智能(AI)和机器学习技术发展迅速。在机器学习系统中,由于读取具有相互关系的庞大数据,有时会在没有可见的理论或逻辑关系的情况下找到连接A事物和B事物的路径。在这种情况下,训练数据的质量对于有效预测的准确性或确定性非常重要。在本文中,我们研究了应用人工智能来预测和决定在不规则波浪中最大化PA-WEC发电的控制力的可能性。人工智能给出的一些结果非常接近不规则波的理论答案。通过检验,我们研究和讨论了在已知波浪中基于理论情况的训练数据集的最佳或有效组合。
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The Fundamental Research on AI Prediction and Determination of Control Force for Maximizing the Power Generation of PA-WEC in Irregular Waves
Marine renewable energy is expected as an alternative energy source to fossil fuel. Wave energy is one of the marine renewable energy. The subject of this paper is maximizing power generation by a Point Absorber Wave Energy Converters (PA-WEC) in irregular waves. In the previous study on maximizing the power generation by one of the authors, it has been shown the theoretical solution of the time-domain control force in irregular waves which are defined by superposing regular waves components. That is, if the PA-WEC can simultaneously understand the irregular incident waves as multi certain regular wave components, it can be decided the control force for maximizing the power generation. On the other hand, it is quite difficult to predict precisely and simultaneously the component of coming irregular waves to a PA-WEC installed in the ocean. In the recent, Artificial Intelligence (AI) and machine learning technology progress rapidly. In the machine learning system, because reading huge data with the relation among them, sometimes it finds out the path connecting A matter and B matter without visible theoretical or logical relations. In this case, the quality of a training data is quite important for accuracy or certainty of the efficient prediction. In this paper, we examine the possibility of applying AI to predict and decide the control force for maximizing the power generation of the PA-WEC in irregular waves. Some of the results given by the AI has been quite close to the theoretical answer in irregular waves. Through the examination, we investigate and discuss the best or the effective combination of training data sets which are based on the theoretical situation in known waves.
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