Data-Driven Comprehensive Security Region Assessment for Hydro–Wind–Solar Hybrid Systems with HVDC Integration

The massive integration of “hydro–wind–solar hybrid” generation transmitted via AC/DC hybrid grids with High-Voltage Direct Current introduces unprecedented challenges to power system transient stability. Traditional Dynamic Security Assessment heavily relies on time-domain simulation and high-density Monte Carlo sampling, which suffer from massive computational burdens and are strictly prohibitive for intra-day operational dispatch. To address this long-standing bottleneck, this paper proposes a novel data-driven comprehensive security region assessment framework based on an Active Learning query strategy and a Support Vector Machine surrogate model. By seamlessly coupling Python with the DIgSILENT PowerFactory simulator, the proposed AL algorithm actively queries and evaluates only the critical operating points near the stability margin, effectively avoiding redundant simulations in obviously safe or unsafe zones. Extensive simulations under a severe N-1-1 contingency demonstrate that the proposed framework can accurately map the non-linear boundaries of both transient rotor angle and short-term voltage stability constraints. Furthermore, the framework’s scalability is rigorously validated in complex 3D high-dimensional operational spaces. By integrating an ϵ-greedy exploration strategy with a Label Flip Rate (LFR) early stopping criterion, the proposed algorithm successfully overcomes the curse of dimensionality. Quantitatively, the proposed method achieves nearly identical boundary resolution using merely 65 physical simulations, as opposed to the 40,000 evaluations required by conventional high-density grid scanning. The total computational time is drastically reduced from 28.6 h to approximately 2.8 min, yielding a remarkable acceleration of over 600 times, making it highly suitable for near-online dynamic security monitoring.