School of Automation, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2019 Mar 28;19(7):1520. doi: 10.3390/s19071520.
In the fault diagnosis process of a photovoltaic (PV) array, it is difficult to discriminate single faults and compound faults with similar signatures. Furthermore, the data collected in the actual field experiment also contains strong noise, which leads to the decline of diagnostic accuracy. In order to solve these problems, a new eigenvector composed of the normalized PV voltage, the normalized PV current and the fill factor is constructed and proposed to characterize the common faults, such as open circuit, short circuit and compound faults in the PV array. The combination of these three feature characteristics can reduce the interference of external meteorological conditions in the fault identification. In order to obtain the new eigenvectors, a multi-sensory system for fault diagnosis in a PV array, combined with a data-mining solution for the classification of the operational state of the PV array, is needed. The selected sensors are temperature sensors, irradiance sensors, voltage sensors and current sensors. Taking account of the complexity of the fault data in the PV array, the Kernel Fuzzy C-means clustering method is adopted to identify these fault types. Gaussian Kernel Fuzzy C-means clustering method (GKFCM) shows good clustering performance for classifying the complex datasets, thus the classification accuracy can be effectively improved in the recognition process. This algorithm is divided into the training and testing phases. In the training phase, the feature vectors of 8 different fault types are clustered to obtain the training core points. According to the minimum Euclidean Distances between the training core points and new fault data, the new fault datasets can be identified into the corresponding classes in the fault classification stage. This strategy can not only diagnose single faults, but also identify compound fault conditions. Finally, the simulation and field experiment demonstrated that the algorithm can effectively diagnose the 8 common faults in photovoltaic arrays.
在光伏（PV）阵列的故障诊断过程中，很难区分具有相似特征的单一故障和复合故障。此外，实际现场实验中采集的数据也包含较强的噪声，这导致诊断精度下降。为了解决这些问题，提出了一种新的特征向量，由归一化的 PV 电压、归一化的 PV 电流和填充因子组成，用于描述光伏阵列中的常见故障，如开路、短路和复合故障。这三个特征特性的组合可以减少故障识别中外在气象条件的干扰。为了获得新的特征向量，需要结合数据挖掘技术对光伏阵列运行状态进行分类，构建一个用于光伏阵列故障诊断的多传感器系统。选择的传感器有温度传感器、辐照度传感器、电压传感器和电流传感器。考虑到光伏阵列故障数据的复杂性，采用核模糊 C 均值聚类方法识别这些故障类型。核模糊 C 均值聚类方法（GKFCM）在对复杂数据集进行分类方面表现出良好的聚类性能，从而可以在识别过程中有效提高分类精度。该算法分为训练和测试两个阶段。在训练阶段，对 8 种不同故障类型的特征向量进行聚类，以获得训练核点。根据训练核点与新故障数据之间的最小欧几里得距离，可以将新的故障数据集识别到故障分类阶段的相应类别中。该策略不仅可以诊断单一故障，还可以识别复合故障情况。最后，通过仿真和现场实验证明，该算法可以有效地诊断光伏阵列中的 8 种常见故障。
