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In this paper, a new hybrid method of change detection and classification is proposed for precise detection and classification of series arc faults (SAFs) in photovoltaic systems. An artificial neural network (ANN) structure is applied for change detection at the first stage, which is then incorporated together with four different convolutional neural network (CNN) models with various dimensions as classifiers for the discrimination of SAFs at the second stage. The models used in the proposed method are 1D CNN, 2D CNN, 3D CNN, and 2D-based images. A comparison of the proposed approach and the state-of-the-art methods has been carried out in terms of accuracy and computational complexity. For a thorough evaluation of the proposed method's performance, studies have been conducted in both simulation and practice, considering various possible scenarios which may emerge. To such an aim, alongside the records from actual measurements in practice, nine models of SAF are also employed for simulation. The results show that the proposed method satisfies principle criteria such as reliability, fault classification error, overfitting, and vanishing solutions. [ABSTRACT FROM AUTHOR]