Research Department Marine Perception, German Research Center for Artificial Intelligence, Marie-Curie-Straße 1, 26129 Oldenburg, Germany.
Institute for Chemistry and Biology of the Marine Environment, Carl von Ossietzky University of Oldenburg, Schleusenstraße 1, 26382 Wilhelmshaven, Germany.
Sensors (Basel). 2023 May 9;23(10):4581. doi: 10.3390/s23104581.
With an increasing number of offshore wind farms, monitoring and evaluating the effects of the wind turbines on the marine environment have become important tasks. Here we conducted a feasibility study with the focus on monitoring these effects by utilizing different machine learning methods. A multi-source dataset for a study site in the North Sea is created by combining satellite data, local in situ data and a hydrodynamic model. The machine learning algorithm DTWkNN, which is based on dynamic time warping and -nearest neighbor, is used for multivariate time series data imputation. Subsequently, unsupervised anomaly detection is performed to identify possible inferences in the dynamic and interdepending marine environment around the offshore wind farm. The anomaly results are analyzed in terms of location, density and temporal variability, granting access to information and building a basis for explanation. Temporal detection of anomalies with COPOD is found to be a suitable method. Actionable insights are the direction and magnitude of potential effects of the wind farm on the marine environment, depending on the wind direction. This study works towards a digital twin of offshore wind farms and provides a set of methods based on machine learning to monitor and evaluate offshore wind farm effects, supporting stakeholders with information for decision making on future maritime energy infrastructures.
随着越来越多的海上风电场的出现，监测和评估风力涡轮机对海洋环境的影响已成为重要任务。在这里，我们进行了一项可行性研究，重点是通过利用不同的机器学习方法来监测这些影响。通过结合卫星数据、本地现场数据和水动力模型，为北海的一个研究地点创建了一个多源数据集。基于动态时间规整和最近邻的机器学习算法 DTWkNN 用于多元时间序列数据插补。随后，进行无监督异常检测以识别海上风电场周围海洋环境中的动态和相互依存关系中可能的推断。异常结果根据位置、密度和时间变化进行分析，提供有关信息并为解释奠定基础。发现使用 COPOD 进行时间检测异常是一种合适的方法。根据风向，有针对性的见解是风力发电场对海洋环境潜在影响的方向和幅度。本研究致力于建立海上风电场的数字孪生，并提供了一套基于机器学习的方法来监测和评估海上风电场的影响，为利益相关者提供信息，以便在未来的海上能源基础设施方面做出决策。
