A compound fault diagnosis model for photovoltaic array based on 1D VoVNet-SVDD by considering unknown faults

As photovoltaic (PV) arrays are exposed to the outdoors year-round, they are susceptible to various faults. The shading condition, degradation or dust coverage can make fault signals more complex, forming compound faults. These faults can lead to a large loss of power generation or irreversible damage to the PV modules, and even fires in severe cases. Moreover, unknown fault types that have never been seen in the training set may occur at actual working conditions. Therefore, accurate diagnosis of various types of single and compound faults (closed-set faults) by considering the identification of unknown faults, namely open-set faults diagnosis, is crucial to improve the efficiency of operation and maintenance. A 1D VoVNet-SVDD based open-set fault diagnosis model for PV arrays is proposed. The model is a two-stage network model consisting of a 1D VoVNet network and a multi-classification Support Vector Data Description (SVDD) in series. The 1D VoVNet network automatically extracts fault features from the input original I-V curve data. These extracted fault features are then combined with environmental parameters to construct the SVDD model. The SVDD identifies known fault types by con-structing a hypersphere for each fault type. Fault types that are not classified into any of the hyperspheres are considered as unknown faults, enabling open-set diagnosis. The experimental results show that the proposed model can accurately classify the closed-set faults among the three designed testing tasks while identify unknown type faults. The comparison demonstrates that the proposed algorithm is superior to the compared models.

Automated summary

This paper proposes an open-set fault diagnosis model for PV arrays based on 1D VoVNet-SVDD. The model accurately diagnoses various types of faults and is capable of identifying unknown fault types.