Lightweight transfer nets and adversarial data augmentation for photovoltaic series arc fault detection with limited fault data

摘要

Incidents of DC series arc faults in Photovoltaic (PV) systems are becoming more common, posing significant threat to properties and human safety. Machine Learning (ML) based methods, developed recently, have demonstrated better performance in many fault diagnosis tasks. However, an unresolved challenge affecting their performance is the problem caused by difference between the source domain data used during the development and the target domain data encountered in operation in the field. Furthermore, the fault data in the targetdomain are usually rare or not available for model training. Another constraint is that complex models are difficult to operate in real-time. This paper proposes a cross-domain DC series arc fault detection framework based on Lightweight Transfer Convolutional Neural Networks with Adversarial Data Augmentation (LTCNNADA) using limited target-domain fault data. Four datasets are prepared using different power sources and inverters in different operating conditions. The proposed framework is validated through comprehensive studies and experiments with different amount of fault data.