Abstract:

The photovoltaic (PV) power station is composed of a large number of photovoltaic modules.Due to the complicated production technology and hard working environment, photovoltaic system DC-side faults occur frequently, directly affecting the photovoltaic system's power generation efficiency.How to extract valid fault sample from the PV array's operating data and identify the fault is an important step to establish a fault model.Therefore, a method based on fuzzy C-means (FCM) clustering to divide and identify the fault samples was proposed.Firstly, the output features of PV array under fault conditions were analyzed and the fault eigenvectors were put forward (open circuit voltage, short circuit current, maximum power point voltage and current).In order to exclude the influence of external excitation conditions on the electrical parameters, the fault eigenvectors were uniformly converted to the standard test condition (STC).Finally, according to the good fuzzy information processing function of FCM, the fault samples of open fault, short fault, shadow fault and abnormal aging fault were clustered.By using the actual operation data, it was proved that this method could accurately and reliably classify the fault samples of the typical fault on the DC side of the PV system and could effectively describe the distribution characteristics of the PV array's electrical parameters in different faults.

Key words: photovoltaic (PV) system, fault samples, fuzzy C-means (FCM) clustering, fault feature extraction