AI-Driven Adaptive Detection for Eliminating Harmonic Oscillations in Inverter-Connected Systems
Date:
In this talk, I presented an AI-driven adaptive approach for detecting and mitigating harmonic oscillations in inverter-connected power systems, an increasingly critical issue as modern grids integrate more inverter-based resources. The method leverages AI techniques, including wavelet-based neural networks, CNNs, and RNNs, to dynamically extract dominant harmonic frequencies and trigger real-time harmonic traps for suppression. Compared with traditional Fourier and wavelet-based methods, the AI-driven models achieved faster and more accurate harmonic detection, demonstrating a scalable and effective solution for enhancing power quality and ensuring stable inverter-based power system operation. The talk can be found here.