Accurate online prediction of wind power is essential for the reliable operation of power systems. However, the precision of wind turbine power forecasts is often hindered by wake effects and inadequate online modeling capabilities. To tackle this issue, this paper proposes a data-driven online prediction method for wind turbine power, leveraging acoustic data for training. The approach involves a neural network that learns acoustic feature changes related to wind output power, by enhancing the kernel extreme learning machine (KELM) technique and applying data augmentation. Additionally, to address the wake effects in real wind fields, active wake control is integrated, considering that most wind turbines operate in the wake of upstream turbines. Experiments using actual wind tunnel measurement data were conducted to assess the performance of the proposed method. The findings demonstrate that this method surpasses other techniques without increasing computational costs. The mean absolute error (MAE) of the proposed method was only 0.4111 and 0.4302, and the root mean square error (RMSE) was just 0.4868 and 0.5195, respectively, in both experiments. Moreover, the proposed acoustic data-driven online power prediction method proves stable in high background noise environments and achieves accurate wind power prediction with just a 10% data sample following the implementation of the active wake control strategy. This work significantly contributes to the efficient utilization of wind energy resources.

中文翻译：

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通过声学数据驱动的在线建模和主动尾流控制进行风电预测


风电功率的准确在线预测对于电力系统的可靠运行至关重要。然而，风力涡轮机功率预测的精度往往受到尾流效应和在线建模能力不足的阻碍。为了解决这个问题，本文提出了一种数据驱动的风力涡轮机功率在线预测方法，利用声学数据进行训练。该方法涉及一个神经网络，通过增强内核极限学习机（KELM）技术和应用数据增强来学习与风输出功率相关的声学特征变化。此外，考虑到大多数风力涡轮机在上游涡轮机的尾流中运行，为了解决实际风场中的尾流效应，集成了主动尾流控制。使用实际风洞测量数据进行实验来评估所提出方法的性能。研究结果表明，该方法在不增加计算成本的情况下超越了其他技术。在两个实验中，该方法的平均绝对误差（MAE）仅为0.4111和0.4302，均方根误差（RMSE）仅为0.4868和0.5195。此外，所提出的声学数据驱动的在线功率预测方法在高背景噪声环境下表现稳定，并且在实施主动尾流控制策略后仅用10％的数据样本即可实现准确的风功率预测。这项工作对风能资源的高效利用做出了重大贡献。