Wake Properties Prediction by means of machine learning algorithms

风力发电已成为最成功的可再生能源，是欧洲仅次于天然气的第二大发电能力(175千兆瓦)。截至2019年底，英国风电累计装机容量为23513兆瓦，发电量超过50太瓦时。尾流转向是一种可以提高风电场性能的控制策略。最近和正在进行的多项研究表明，偏航的上风涡轮机由于减少了尾迹赤字，可以为下风涡轮机提供更多的电力。使用逆风涡轮机的降额，或通过偏航控制和降额的组合，可能会达到类似的效果。为了有力地应用尾迹操纵或风力涡轮机降级，有必要了解涡轮机是否确实正在经历任何有害的尾迹影响以及尾迹扰动的相对位置，以便能够实施适当的控制行动。使用机舱安装的激光雷达和其他遥感技术识别尾迹特征的例子有很多种。结果是有希望的，然而，他们需要额外的设备(激光雷达)，这增加了成本和复杂性，如果它安装在每个涡轮机上。进一步的问题是相对较低的数据可用性和对数据处理的要求。此外，更可取的是，可以使用标准传感器设置进行预测。利用涡轮机安装的传感器(例如应变计)，辅以加速度计数据和SCADA信号(如转速和功率)，成功地表征了风切变和偏航偏心等风流入条件。由于尾迹缺陷引起的风廓线与风切变以及风速和涡流的其他变化有些相似的现象，预计尾迹效应将导致负载谐波的类似变化。机器学习已经成功地应用于风力发电机的各种建模和检测目的，到目前为止，最大的重点是功率输出预测和故障检测。对于尾迹特性预测，问题类似于故障检测，因为需要识别和评估信号中的模式是否与正常行为一致。因此，机器学习被认为是实现基于负载信号的自动尾迹特性预测的最佳途径。该项目的主要目标是设计一种基于受尾迹影响的涡轮机测量信号的风力机尾迹特性预测方法。尾迹特性预测功能将能够控制单个风力涡轮机，以最大限度地提高风力发电场的性能。这是通过以下具体目标实现的：-使用尾流条件和风力涡轮机动力学的数值模拟设计基于机器学习的尾流特性预测算法-分析特征重要性，以便理解和包括相关输入；此外，可以基于个人输入对更准确预测的贡献来降低维度。此外，了解哪些输入在整个涡轮机机群中可用，并反映这些配置并针对所考虑的模型进行评估。通过与其他预测功能(如机舱安装的激光雷达)进行现场验证，提出利用尾迹特性预测信息来改进风力发电场性能的控制方案

Wind power has become the most successful renewable energy and represents the second-largest power generation capacity (175 GW) in Europe after natural gas. At the end of 2019, there was an installed accumulative wind power capacity in the UK of 23,513 MW of wind power, generating more than 50 TWh of energy.Wake steering is a control strategy that can increase wind farm performance. There are multiple recent and ongoing studies, showing that yawing upwind turbines can deliver more power for the downwind turbines due to reduction of wake deficits. Similar effect can potentially be achieved using derating of upwind turbines, or by a combination of yaw control and derating. In order to robustly apply wake steering or wind turbine derating, it is necessary to know whether the turbine is actually experiencing any detrimental wake effects and the relative location of the wake disturbance so that appropriate control action can be carried out.There are multiple examples of how wake characteristics have been identified using nacelle-mounted lidars and other remote sensing technologies. The results are promising however they require additional devices (lidars) which increase cost and complexity if it were to be installed on each turbine. Further issues are relatively low data availability and the requirements for data processing. Furthermore it would be preferable for prediction can be carried out with standard sensor setups. Wind inflow conditions such as wind shear and yaw misalignment have been successfully characterized with turbine-mounted sensors, e.g. strain gages, supplemented with accelerometer data and SCADA signals such as rpm and power. Since the wind profiles caused by wake deficits represent somewhat similar phenomena to wind shear and other variations of wind speed and turbulence over the rotor, it is expected that wake effects will cause similar variations in the load harmonics.Machine learning has been successfully applied for various modelling and detection purposes in wind turbines, with the greatest focus by far being power output prediction and detection of faults. For wake properties prediction, the problem is similar to fault detection since patterns in the signal need to be identified and evaluated as to whether or not they are consistent with normal behavior. Machine Learning is, thus, expected to be the best approach for achieving automated wake properties prediction based on load signals.The main goal of the project is to devise a method for wind turbine wake properties prediction based on the use of measurement signals from the turbine affected by the wake. The wake properties prediction capability will enable controlling individual wind turbines to maximize wind farm performance. This is achieved through the following specific objectives:- Devise a Machine Learning-based wake properties prediction algorithm using numerical simulations of wake conditions and wind turbine dynamics- Analyze feature importance so that relevant inputs are understood and included; additionally dimensionality may be reduced based on individual input contribution to a more accurate prediction. Furthermore understand which inputs are available across turbine fleet, and reflect on these configuration and evaluate in relation to considered models.Carry out field validation of the prediction method by comparison with other prediction capabilities such as nacelle-mounted lidars- Suggest a control scheme to utilize the wake properties prediction information for improving wind farm performance