System State Estimation

System state estimation aims to determine the operating conditions of power systems, crucial for reliable grid management and control. Recent research heavily emphasizes data-driven approaches, employing deep learning architectures like graph neural networks and transformers, often incorporating physics-informed constraints to improve accuracy and efficiency. This focus on advanced machine learning techniques offers the potential for faster, more robust, and scalable state estimation, particularly beneficial for large-scale and increasingly complex power grids with high penetration of renewable energy sources. The resulting improvements in accuracy, speed, and robustness have significant implications for grid monitoring, control, and optimization.