Compound fault diagnosis model for Photovoltaic array using multi-scale SE-ResNet

• A multi-scale SE-ResNet is used to diagnose compound faults for PV array under dust covering. • A multi-scale receptive field fusion module is proposed to extract finer features. • The proposed model can estimate the degree of dust coverage on PV array. • The performance of the proposed model is superior to those of 1D-ResNet and ResNet18. • The proposed model can be transfered to the condition of dust accumulate on the bottom of the PV panels. Photovoltaic (PV) systems working outdoors are susceptible to various faults. The deposition of dust on the PV array may make these faults more complicated, resulting in a kind of compound faults. The similarity between compound faults and single faults leads to their misclassification. Therefore, accurately detection of potential PV array compound faults is essential to improve the operating efficiency and safety of PV systems. Addressing the above situation, this paper proposes a fault diagnosis (FD) scheme for PV array using a multi-scale SE-ResNet network. In addition, a multi-scale receptive field fusion module (MRFF) is designed to improve the diagnostic performance of the model. This model can automatically extract multi-scale fault features from input raw current–voltage curves data and environmental parameters. The single faults, partial shading conditions (PSCs), and compound faults under different degrees of dust covering can be diagnosed. In addition, the dust coverage degree is estimated simultaneously, which can provide a basis for developing a cleaning schedule. Simulation and experiment results demonstrate the superior performance of this method compared with other approaches, and also indicate the proposed model can be applied to the condition of dust accumulate on the bottom of the PV panels through transfer learning.