Geology

Voltage

Mathematics

Computer science

Photovoltaic system

Gene

Arc (geometry)

Engineering

Transformation (genetics)

Mechanical engineering

Biochemistry

Short circuit

Algorithm

Arc-fault circuit interrupter

Smart grid

Domain (mathematical analysis)

Seismology

Fault (geology)

Grid

Geometry

Mathematical analysis

Chemistry

Electronic engineering

Electrical engineering

IEEE Access

DC arc faults, especially series arcing, can occur in photovoltaic (PV) systems and pose a challenging detection and protection problem. Machine learning-based methods are increasingly being used for fault diagnosis applications. However, the performance of such detection algorithms will degrade because of variations between the source domain data used during the development and the target domain data encountered in operation of the field. Furthermore, the fault's data in the target domain for model training are usually not available. In this paper, domain adaptation combined with deep convolutional generative adversarial network (DA-DCGAN)-based methodology is proposed, where DA-DCGAN first learns an intelligent normal-to-arcing transformation from the source-domain data. Then by generating dummy arcing data with the learned transformation using the normal data from the target domain and employing domain adaptation, a robust and reliable fault diagnosis scheme can be achieved for the target domain. The PV lo...

DA-DCGAN: An Effective Methodology for DC Series Arc Fault Diagnosis in Photovoltaic Systems