Normal Behaviour Models for Wind Turbine Vibrations: Comparison of Neural Networks and a Stochastic Approach
Normal Behaviour Models for Wind Turbine Vibrations: Comparison of Neural Networks and a Stochastic Approach
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DOI：
10.3390/en10121944
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发表时间：
2017-11
期刊：
影响因子：
3.2
通讯作者：
P. Lind;L. Vera-Tudela;M. Wächter;M. Kühn;J. Peinke
中科院分区：
文献类型：
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作者：
P. Lind;L. Vera-Tudela;M. Wächter;M. Kühn;J. Peinke
为了监测风力涡轮机振动，建立正常行为模型以预测塔顶加速度和传动系振动。与模型预测的信号偏差被标记为异常，并进一步调查。在本文中，我们评估了一种随机的方法来重建1 Hz的塔顶加速度信号，这是在风力涡轮机位于德国北海的阿尔法Ventus风电场测量。我们比较所得到的数据重建与基于神经网络的模型，这是以前报道的数据挖掘算法，适合于重建这个信号。我们的研究结果表明，随机方法在高频域（1 Hz）优于神经网络。虽然神经网络检索准确的步进预测，具有较低的均方误差，但随机方法预测更好地保留了原始信号的统计数据和频率分量，保持了高精度水平。我们的随机方法的实现是开放源代码，可以很容易地适应其他情况下，涉及随机数据重建。基于我们的研究结果，我们认为，这种方法可以实现在信号重建监测的目的或异常行为检测。
To monitor wind turbine vibrations, normal behaviour models are built to predict tower top accelerations and drive-train vibrations. Signal deviations from model prediction are labelled as anomalies and are further investigated. In this paper we assess a stochastic approach to reconstruct the 1 Hz tower top acceleration signal, which was measured in a wind turbine located at the wind farm Alpha Ventus in the German North Sea. We compare the resulting data reconstruction with that of a model based on a neural network, which has been previously reported as a data-mining algorithm suitable for reconstructing this signal. Our results present evidence that the stochastic approach outperforms the neural network in the high frequency domain (1 Hz). Although neural network retrieves accurate step-forward predictions, with low mean square errors, the stochastic approach predictions better preserve the statistics and the frequency components of the original signal, retaining high accuracy levels. The implementation of our stochastic approach is available as open source code and can easily be adapted for other situations involving stochastic data reconstruction. Based on our findings we argue that such an approach could be implemented in signal reconstruction for monitoring purposes or for abnormal behaviour detection.