The recent developments in the research area of network and power system control and estimation highlight a significant shift towards leveraging local information and adaptive methodologies to enhance system resilience, efficiency, and accuracy. A common theme across the studies is the exploration of control and estimation strategies that rely on limited or local state information, aiming to reduce the computational and communication overhead while maintaining or even improving system performance. This is particularly evident in the context of power grid synchronization and fault location, where innovative approaches have been proposed to achieve precise control and accurate fault detection with minimal data. Additionally, there is a growing emphasis on the development of robust and adaptive control systems capable of withstanding network anomalies and disturbances, ensuring system stability and reliability under adverse conditions. The integration of advanced computational techniques, such as machine learning and Bayesian hierarchical models, into power system analysis and optimization represents another key trend, offering new avenues for handling the complexity and uncertainty inherent in modern power networks. These advancements not only contribute to the theoretical understanding of network and power system dynamics but also provide practical tools and methodologies for improving system design, operation, and security.

Noteworthy Papers

• Global network control from local information: Introduces a control approach using limited state information neighborhoods, demonstrating efficient control of large networks with minimal performance loss.
• Resilient Distributed Control for Uncertain Nonlinear Interconnected Systems under Network Anomaly: Presents a distributed adaptive control methodology ensuring system stability in the presence of network anomalies, with defined resilience conditions.
• A New Underdetermined Framework for Sparse Estimation of Fault Location for Transmission Lines Using Limited Current Measurements: Proposes a novel fault location method using current measurements and the robust YALL1 algorithm, enhancing accuracy and robustness.
• A Key Conditional Quotient Filter for Nonlinear, non-Gaussian and non-Markovian System: Develops a novel filter for state estimation in complex systems, showing superior accuracy compared to existing filters.
• SafePowerGraph-LLM: Novel Power Grid Graph Embedding and Optimization with Large Language Models: Introduces a framework for solving OPF problems using LLMs, demonstrating the potential of machine learning in power system optimization.