Compressive Sensing-Based Missing-Data-Tolerant Fault Detection for Remote Condition Monitoring of Wind Turbines
Compressive Sensing-Based Missing-Data-Tolerant Fault Detection for Remote Condition Monitoring of Wind Turbines
复制标题
基于压缩感知的风电机组远程状态监测容错故障检测
DOI：
10.1109/tie.2021.3057039
复制
发表时间：
2022-02
影响因子：
7.7
通讯作者：
Yayu Peng;W. Qiao;Liyan Qu
中科院分区：
文献类型：
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作者：
Yayu Peng;W. Qiao;Liyan Qu
与传统的现场风力涡轮机状态监测系统（CMS）相比，远程CMS可以使用更好的计算资源以更先进的算法处理数据，因此可以提供更先进的状态监测能力，但可能会遇到数据丢失问题，特别是在使用无线数据传输时。为了解决这个问题，本文提出了一种基于压缩感知的容错数据故障检测方法，用于风力发电机组的远程状态监测。首先，对从风力涡轮机收集的状态监测信号进行调节以增加其稀疏性。然后，设计基于压缩感知的采样算法来对条件信号进行采样。所得到的数据样本（称为调节信号的测量值）通过无线传输，在此期间一些数据样本可能会丢失。在数据接收端，通过基于压缩感知的信号重建算法从接收到的数据样本（可能不完整）重建调节信号。最后，对重构信号进行频谱分析，通过故障特征频率识别进行风力发电机故障检测。通过使用从每个风力涡轮机远程收集的发电机电流信号数据，同时考虑不同的数据丢失率，对所提出的方法进行了验证，用于 Skystream 3.7 风力涡轮机和 Air Breeze 风力涡轮机的轴承故障检测。
Compared with traditional onsite wind turbine condition monitoring systems (CMSs), the remote CMSs can use better computational resources to process data with more advanced algorithms and, thus, can provide more advanced condition monitoring capabilities, but may suffer from a data loss problem, especially when wireless data transmission is used. To solve this problem, this article proposes a compressive sensing-based missing-data-tolerant fault detection method for remote condition monitoring of wind turbines. First, the condition monitoring signals collected from wind turbines are conditioned to increase their sparsity. Then, a compressive-sensing-based sampling algorithm is designed to sample the conditioned signals. The resulting data samples, called measurements of the conditioned signals are transmitted wirelessly during which some data samples are possibly lost. At the data receiving end, the conditioned signals are reconstructed from the received data samples, which might be incomplete, via a compressive-sensing-based signal reconstruction algorithm. Finally, spectrum analysis is performed on the reconstructed signals for wind turbine fault detection via fault characteristic frequency identification. The proposed method is validated for bearing fault detection of a Skystream 3.7 wind turbine and an Air Breeze wind turbine by using the data of a generator current signal collected from each wind turbine remotely while considering different data loss rates.