Machine Learning-Assisted Monitoring and Damping Control of Low-Frequency Oscillations in Power Systems

Abstract—Low-frequency oscillations (LFOs) pose a significant threat to the dynamic stability and reliable operation of large interconnected power systems. Traditional damping controllers often face challenges in adapting to nonlinear system behavior, varying load conditions, and network topology changes. This paper presents a machine learning-assisted framework for the monitoring and damping control of low-frequency oscillations in power systems. The proposed approach integrates advanced data-driven techniques to identify oscillatory modes, predict instability patterns, and optimize control parameters in real time. A hybrid model combining signal processing for feature extraction and supervised learning for oscillation prediction is developed to enhance situational awareness and decision-making. Simulation studies on standard IEEE test systems validate the proposed method’s ability to improve damping performance, reduce oscillation amplitude, and enhance overall system stability under dynamic operating conditions. The results demonstrate that machine learning-based adaptive control offers a robust and scalable solution for mitigating low-frequency oscillations in next-generation smart power grids.

Low-Frequency Oscillations, Power System Stability, Machine Learning, Damping Control, Signal Processing, Adaptive Control, Smart Grid, Real-Time Monitoring

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