Wind turbines abnormality detection through analysis of wind farm power curves
Wind turbines abnormality detection through analysis of wind farm power curves
复制标题
通过分析风电场功率曲线进行风力发电机异常检测
DOI：
10.1016/j.measurement.2016.07.006
复制
发表时间：
2016-11
期刊：
影响因子：
5.6
通讯作者：
Liu, Chengliang
中科院分区：
文献类型：
--
作者：
Wang, Shuangyuan;Huang, Yixiang;Li, Lin;Liu, Chengliang
异常检测和预测是早期发现涡轮机故障、避免灾难发生的关键技术。在这项研究中，我们提出了一种新的异常检测和预测技术的基础上异构的信号和信息，如输出功率信号和风力涡轮机停机事件信息收集的监控和数据采集（SCADA）系统。首先，在时域和频域中对功率信号进行判别统计特征提取。然后，基于四分位数导出归一化统计数据的边带表达式。此外，定义了一个相异性度量来计算停机时间间隔之间的距离，得到了一个更高维的特征空间。为了降低特征空间的维数，拉普拉斯特征映射（LE）的非线性降维方法的实施。然后，采用线性混合自组织映射（LMSOM）分类器对异常类型进行分类，并采用累积趋势差法对涡轮机故障进行预测。该方法的验证和应用从中国北方风电场收集的数据。结果表明，该方法能有效地检测和预测涡轮机的异常。
Abnormality detection and prediction is a critical technique to identify wind turbine failures at an early stage, thus avoiding catastrophes. In this study, we propose a new abnormality detection and prediction technique based on heterogeneous signals and information, such as output power signals and wind turbines downtime event information collected from the supervisory control and data acquisition (SCADA) system. First, discriminant statistical feature extraction is performed on the power signals in both the time-domain and frequency-domain. Then, a sideband expression is derived for normalized statistical data based on quartiles. In addition, a dissimilarity metric is defined to calculate the distances between downtime time intervals, and a higher dimension feature space is obtained. To reduce the dimension of the feature space, the Laplacian Eigenmaps (LE) nonlinear dimensionality reduction method is implemented. Afterwards, a Linear Mixture Self-organizing Maps (LMSOM) classifier is applied to differentiate abnormal types and a cumulative trend difference method is utilized to predict the faults in wind turbine. The method is validated and applied to data collected from a wind farm in north China. The results show that the proposed technique can effectively detect and predict wind turbine abnormalities.
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