Abstract

The increasing integration of inverter-based systems in modern power grids has introduced significant power quality challenges, particularly harmonic oscillations that can degrade system stability and efficiency. This paper proposes an AI-driven adaptive detection approach to identify and mitigate harmonic oscillations in inverter-connected systems. By leveraging artificial intelligence methods, the system can dynamically extract dominant harmonic frequencies and activate appropriate harmonic traps to suppress oscillations in real time. The proposed method explores various AI techniques, including wavelet transform-based neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), to enhance accuracy and adaptability. Comparative evaluations with traditional Fourier and wavelet-based approaches demonstrate the effectiveness of the AI-driven solution in achieving faster and more precise harmonic suppression. This research contributes to the advancement of intelligent power quality management, offering a scalable and efficient approach to ensure stable inverter-based power systems.

Recommended citation: Nguyen-Vinh, K., Hoang, M., & Gono, R. (2025). AI-Driven Methods for Eliminating Harmonic Oscillations in Inverter-Connected Systems. IEEE ICECIE 2025.
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