Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions

Abstract

Automatic fault detection and diagnosis techniques for photovoltaic arrays are crucial to promote the efficiency, reliability and safety of photovoltaic systems. In recent decades, many conventional artificial intelligence approaches have been successfully applied to automatically establish fault detection and diagnosis model using fault data samples, but most of them rely on manual feature extraction or expert knowledge to build diagnosis models, which is inefficient and may ignore some potential useful features. In addition, they usually use shallow neural networks with limited performance. Addressing the issues, this paper proposes a novel intelligent fault detection and diagnosis method for photovoltaic arrays based on a newly designed deep residual network model trained by the adaptive moment estimation deep learning algorithm, which can automatically extract features from raw current-voltage curves and ambient irradiance and temperature, and effectively improve the performance with a deeper network. In order to validate the proposed fault diagnosis model, a Simulink based simulation model is designed for a real laboratory photovoltaic array, and both fault simulation and real experiments are carried out to obtain simulation and experimental fault datasets. Furthermore, two other popular deep learning based models are used for comparison, including convolution neural network and convolutional auto-encoder. Both of simulation and real experimental comparison results demonstrate that the proposed deep residual network based method achieves high and best overall performance in terms of accuracy, generalization performance, reliability and training efficiency.