Artificial Neural Networks for Parameter Optimization in Power Systems Stability Control

Renewable energy sources (RES) such as wind turbines (WTs) and photovoltaic (PV) systems possess dynamic characteristics that can negatively influence the stability of the power systems they are integrated into. Among the various approaches to address these challenges, the optimization of system component parameters stands out as both economically and technically effective. This study introduces an Artificial Neural Network (ANN)-based method for determining the optimal parameters of critical system components, including Power System Stabilizers (PSS) and the control systems of PV and WT units. Through a comprehensive evaluation of system performance under diverse operating scenarios, the proposed approach provides a deeper and more accurate insight into system dynamics. Time-domain simulations confirm the effectiveness of the ANN-optimized Battery Energy Storage System (BESS), Power System Stabilizer (PSS) and RES test system in mitigating power system oscillations.