基于动态特征矩阵的k近邻风电机组故障检测方法

受风的间歇性和随机性影响风电机组运行状态频繁切换,导致设备状态异常检测误报和漏报情况严重,风电企业运维成本居高不下。为此,提出了基于动态特征矩阵的k近邻故障检测方法,该方法采用基于互信息的动态特征矩阵描述风电机组的动态特性,通过加权k近邻同时考虑动态特征矩阵中的特征贡献率与累计互信息的影响,利用动态阈值计算降低运行状态突变造成的误报。分别以美国可再生能源实验室5 MW海上风机基准模型的常见传感器和执行器故障以及SCADA数据中记录的变桨系统故障为例,将所提方法的故障检测结果分别与PCA、KPCA、FD-kNN以及PC-kNN故障检测方法进行对比,结果表明所提方法能够准确进行故障信息的检测,所提方法优于其他对比故障检测方法。

Fault detection of wind turbines using k nearest neighbor based on dynamic feature matrix

The intermittency and randomness of wind make the operation state of wind turbine change frequently. As a result, the false positive ration and false negative rate in anomaly detection of equipment are serious. The costs of operation and maintenance in the wind power industry are high. To solve this problem, one kind of K nearest neighbor fault detection method based on dynamic feature matrix is proposed in this work. It constructs a dynamic feature matrix based on mutual information to describe the dynamic characteristics of wind turbine. The weighted k nearest neighbor fault detection method is introduced to address the influence of the characteristic contribution and cumulative mutual information in dynamic feature matrix. The dynamic threshold can help reduce false alarm caused by the sudden change of operation state. This paper takes examples of the common sensor faults and actuator faults in the 5MW offshore benchmark of National Renewable Energy Laboratory and the pitch system faults in SCADA system. The fault detection results of the proposed method are compared with PCA, KPCA, FD kNN and PC kNN, respectively. Experimental results demonstrate that the proposed method can accurately detect the fault information. Compared with other methods, it can achieve better fault detection results.