The effectiveness of cybersecurity research for SCADA systems depends on access to high-quality network traffic data, yet such data remains scarce due to proprietary restrictions and security concerns. Synthetic data generated by machine learning models, particularly Generative Adversarial Networks (GANs), presents a promising alternative. This study provides a preliminary evaluation of GAN-based approaches for SCADA network traffic synthesis using the IEEE ITACHA DNP3 Smart Grid dataset. We compare a general-purpose GAN (CTGAN) with a network-traffic-specific GAN (NetShare) based on fidelity and statistical consistency. Initial results indicate that CTGAN generates statistically diverse synthetic data, while NetShare suffers from excessive duplication, limiting its applicability. These findings offer an early structured roadmap for selecting and refining generative models for SCADA data synthesis, supporting future research in smart grid security.