Power system operators face growing challenges evaluating grid security as renewable energy penetration increases and data sources become more fragmented. Traditional assessment methods struggle with inconsistent data formats and evolving stability criteria in modern grids with solar, wind, and battery storage resources.
Researchers have developed an integrated framework addressing these challenges by combining artificial intelligence with advanced network analysis. The system uses a large language model to normalize and standardize operational data from disparate sources—a critical first step since utilities often work with inconsistent reporting formats. The model achieved 97.49% accuracy in data standardization, significantly reducing manual workflow design time.
Once data is standardized, a dual-head graph neural network analyzes the power system topology to predict vulnerability at both individual buses and transmission lines. The framework calculates a unified security-stability score based on expected load-shedding across the network, incorporating realistic operational constraints including equipment failure probabilities and renewable-induced weak-grid effects measured by short-circuit ratio (SCR). This approach also evaluates the system's ability to withstand single contingencies—the loss of any major component.
The dual-head architecture separately predicts node-level and edge-level security scores, outperforming traditional single-head approaches in accuracy. Case studies demonstrate that the SCR-weighted scoring method provides more conservative risk assessment aligned with actual renewable-related instabilities. The framework enables operators to quickly identify vulnerable network locations and prioritize mitigation measures.
This development is particularly relevant as distribution networks increasingly connect distributed energy resources and utilities seek to automate complex stability assessments. The ability to process heterogeneous data and generate interpretable security predictions could help grid operators maintain reliability while accommodating higher renewable penetration levels.
