Simulation Analysis of a Photovoltaic Grid-Connected Harmonic Filter Based on Adaptive Neural Networks

Addressing the slow response issues of current technologies in managing dynamic harmonic environments in distributed photovoltaic grid-connected systems, this paper proposes a harmonic filter model based on adaptive neural networks. This filter is designed on an improved Long Short-Term Memory network model, capable of real-time harmonic identification and filtering through self-learning and adaptive adjustment, thus enhancing the filter's performance and stability. Simulation experiments conducted on the Simulink platform revealed three key findings: first, the total harmonic distortion of harmonic current can be significantly reduced from an initial high value to below 3%; second, the response time is within 10 ms, significantly outperforming traditional filters; third, the filtering efficiency remains above 90%, indicating substantial advantages in harmonic management. Its high-efficiency filtering effect, rapid dynamic response capability, and intelligent adaptive capabilities demonstrate high potential in improving photovoltaic grid-connected harmonic management.