A Dual-Channel Enhanced Mamba Model for Fault Detection in Grid-Connected Photovoltaic Systems

Accurate fault detection is essential for the safe and reliable operation of grid-connected photovoltaic (PV) systems under complex and dynamically varying conditions. However, existing data-driven approaches are often hindered by the scarcity of labeled fault data and by their limited ability to model complex multivariate temporal dependencies. To address these challenges, this paper first develops a realistic simulation of a grid-connected PV system to generate a large volume of labeled multivariate time-series fault data spanning diverse fault scenarios under varying operating conditions. The simulated data augment the limited real-world measurements, improving fault coverage and model generalization. On this basis, a dual-channel enhanced Mamba model is proposed for PV fault detection. The model decouples temporal modeling and variable-wise modeling into two dedicated channels, enabling complementary extraction of global temporal dependencies and intra-variable dynamics. Extensive experiments show that the proposed approach consistently outperforms several mainstream time-series classification methods in accuracy, precision, recall, and F1-score, demonstrating that it provides an effective and scalable solution for data-driven fault detection in grid-connected PV systems.