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Various faults can occur during the operation of PV arrays, and both thedust-affected operating conditions and various diode configurations make thefaults more complicated. However, current methods for fault diagnosis based onI-V characteristic curves only utilize partial feature information and oftenrely on calibrating the field characteristic curves to standard test conditions(STC). It is difficult to apply it in practice and to accurately identifymultiple complex faults with similarities in different blocking diodesconfigurations of PV arrays under the influence of dust. Therefore, a novelfault diagnosis method for PV arrays considering dust impact is proposed. Inthe preprocessing stage, the Isc-Voc normalized Gramian angular differencefield (GADF) method is presented, which normalizes and transforms the resampledPV array characteristic curves from the field including I-V and P-V to obtainthe transformed graphical feature matrices. Then, in the fault diagnosis stage,the model of convolutional neural network (CNN) with convolutional blockattention modules (CBAM) is designed to extract fault differentiationinformation from the transformed graphical matrices containing full featureinformation and to classify faults. And different graphical featuretransformation methods are compared through simulation cases, and differentCNN-based classification methods are also analyzed. The results indicate thatthe developed method for PV arrays with different blocking diodesconfigurations under various operating conditions has high fault diagnosisaccuracy and reliability.

Fault diagnosis for PV arrays considering dust impact based on  transformed graphical feature of characteristic curves and convolutional  neural network with CBAM modules