The field of power systems is moving towards the integration of advanced control strategies and data-driven approaches to ensure stability and efficiency. Researchers are exploring new methods for analyzing and controlling complex power systems, including the use of machine learning and optimization techniques. A key focus area is the development of decentralized stability conditions and data-driven control frameworks that can handle the increasing penetration of renewable energy sources and other distributed resources. Notable papers in this area include 'Data-Driven Analysis and Predictive Control of Descriptor Systems with Application to Power and Water Networks', which presents a comprehensive data-driven framework for analyzing and controlling descriptor systems, and 'One Equation to Rule Them All -- Part I: Direct Data-Driven Cascade Stabilisation', which introduces a framework for direct data-driven control for general problems involving interconnections of dynamical systems. Additionally, 'Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy' proposes a hybrid machine learning and reinforcement learning framework for predicting grid stability and optimizing control actions.
Advances in Power System Stability and Control
Sources
Data-Driven Analysis and Predictive Control of Descriptor Systems with Application to Power and Water Networks
Resilient Control for Networked Switched Systems With/Without ACK: An Active Quantized Framework
Wide-Area Power System Oscillations from Large-Scale AI Workloads
Geometric Decentralized Stability Condition for Power Systems Based on Projecting DW Shells
One Equation to Rule Them All -- Part I: Direct Data-Driven Cascade Stabilisation
One Equation to Rule Them All -- Part II: Direct Data-Driven Reduction and Regulation
Linear Power System Modeling and Analysis Across Wide Operating Ranges: A Hierarchical Neural State-Space Equation Approach
A Data-Driven Forced Oscillation Locating Method for Power Systems with Inverter-Based Resources
AI Data Centers Need Pioneers to Deliver Scalable Power via Offgrid AI
A Learning-based Hybrid System Approach for Detecting Contingencies in Distribution Grids with Inverter-Based Resources
Globally Stable Discrete Time PID Passivity-based Control of Power Converters: Simulation and Experimental Results
Towards Reliable Neural Optimizers: Permutation-Equivariant Neural Approximation in Dynamic Data Driven Applications Systems
Comparison of Droop-Based Single-Loop Grid-Forming Wind Turbines: High-Frequency Open-Loop Unstable Behavior and Damping
Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy
Transient Stability Analysis of a Hybrid Grid-Forming and Grid-Following RES System Considering Multi-Mode Control Switching
Missing Money and Market-Based Adequacy in Deeply Decarbonized Power Systems with Long-Duration Energy Storage