Aiming to address the specific issues of frequent faults in photovoltaic (PV) power stations located in grassland and pastoral areas, particularly those caused by biological damage to PV equipment, as well as the challenges of insufficient fault samples and missing data labels in real-world scenarios, a novel fault diagnosis network was proposed: the fusion pseudo-label and encoder-generator adversarial network with meta-learning vision transformer (FEG – MLViT). Firstly, the collected time-series data was transformed by using continuous wavelet transform to extract time-frequency features from onedimensional signals, thereby enhancing the representation of nonlinear characteristics. Then an encodergenerator adversarial network integrated with a pseudo-labeling mechanism was designed to generate labeled fault data. In addition, a novel three-dimensional comprehensive evaluation metric was proposed to assess both real and generated samples, addressing the limitations of traditional single-dimensional evaluation indicators. Finally, a meta-learning-based vision transformer (MLViT) diagnostic network was constructed, leveraging its global feature extraction and rapid adaptation capabilities to achieve highprecision fault diagnosis. The proposed method was validated by using real PV operational data. Comparative experiments with state-of-the-art approaches demonstrated the feasibility and effectiveness of the proposed model, achieving an average diagnostic accuracy of 95.56% across five fault categories. Moreover, the model converged with as few as nine samples, indicating its strong performance in few-shot fault diagnosis scenarios.