A State-Space Estimation and Control of Power System Oscillations

Abstract

Inter-area oscillations provide a considerable risk to the stability of interconnected power networks, as they involve generators from many regions, potentially resulting in intensified oscillations in tie-lines. The prompt and efficient suppression of these oscillations—preferably within 15 seconds—is essential for the secure functioning of contemporary power networks. Traditional control methods may fail to provide uniform performance under diverse operating conditions, underscoring the necessity for sophisticated estimation and control strategies. In this research, a novel approach combining subspace identification methods, specifically the N4SID and MOESP algorithms, with a Linear Quadratic Regulator (LQR) controller is proposed to address the challenge of inter-area oscillations. Subspace identification methods, known for their ability to extract accurate state-space models directly from input-output data, enable efficient and robust estimation of the power system’s dynamic behavior under diverse post-disturbance scenarios. By leveraging the LQR controller, optimal control efforts are achieved, ensuring rapid damping of oscillations and enhanced system stability. The proposed framework eliminates the need for manual re-tuning of controller parameters and demonstrates superior performance in both computational efficiency and control effectiveness compared to traditional methods. Simulation results validate the efficacy of this combined approach, offering a reliable and computationally efficient solution for inter-area oscillation damping in interconnected power systems.