The integration of high-penetration renewable energy sources (RESs) into transmission networks introduces profound uncertainty that challenges traditional infrastructure planning approaches. Existing transmission expansion planning (TEP) models either rely on static scenario sets or over-conservative worst-case assumptions, failing to capture the operational stress triggered by rare but structurally impactful renewable behaviors. This paper proposes a novel bi-level optimization framework for transmission planning under adversarial uncertainty, coupling a distributionally robust upper-level investment model with a lower-level operational response embedded with physics and market constraints. The uncertainty space was not exogenously fixed, but instead dynamically generated through a physics-informed spatiotemporal generative adversarial network (PI-ST-GAN), which synthesizes high-risk renewable and load scenarios designed to maximally challenge the system’s resilience. The generator was co-trained using a composite stress index—combining expected energy not served, loss-of-load probability, and marginal congestion cost—ensuring that each scenario reflects both physical plausibility and operational extremity. The resulting bi-level model was reformulated using strong duality, and it was decomposed into a tractable mixed-integer structure with embedded adversarial learning loops. The proposed framework was validated on a modified IEEE 118-bus system with high wind and solar penetration. Results demonstrate that the GAN-enhanced planner consistently outperforms deterministic and stochastic baselines, reducing renewable curtailment by up to 48.7% and load shedding by 62.4% under worst-case realization. Moreover, the stress investment frontier exhibits clear convexity, enabling planners to identify cost-efficient resilience strategies. Spatial congestion maps and scenario risk-density plots further illustrate the ability of adversarial learning to reveal latent structural bottlenecks not captured by conventional methods. This work offers a new methodological paradigm, in which optimization and generative AI co-evolve to produce robust, data-aware, and stress-responsive transmission infrastructure designs.

Power system transient stability is a challenge when integrating large-scale wind turbines into weak grids. This paper addresses the issue of transient stability in such situations by optimizing a wind turbine’s active current behavior. A wind turbine’s active current reference controller and its setting optimization method are proposed based on analyses of two associated problems: the mechanism for improving transient stability of a single (synchronous) machine infinite bus (SMIB) system, as well as the various physical factor dependencies dictating how active and reactive wind turbine currents affect the swing dynamics of synchronous machines. Analysis of the first problem guided the design of the controller’s main structure. Analysis of the second problem guided selection of the control object within a wind turbine’s active and reactive currents, as well as helped recognition of the influential physical factors that must be considered in the parameter setting process. The efficiency of the controller and the validity of the analyses were verified by case studies using Kundur’s two-area system.