Unbalanced Power Distribution Grid

Unbalanced power distribution grids, characterized by unequal current flow across phases due to factors like unevenly distributed renewable energy sources and loads, pose significant challenges for grid management and security. Current research focuses on developing advanced algorithms, including physics-informed neural networks (like convolutional autoencoders), adversarial autoencoders, and graph neural networks, to improve anomaly detection (e.g., cyberattacks) and enhance power flow analysis in these complex systems. These data-driven approaches aim to address limitations of traditional methods, particularly in handling the nonlinearity and uncertainty introduced by distributed energy resources. Improved accuracy and efficiency in power flow calculations and anomaly detection are crucial for ensuring reliable and secure operation of modern power grids.