Multi-Level Frequency Security Early Warning Technology Based on Deep Learning in New Power Systems

With the large-scale integration of renewable energy, the frequency security of the power system is facing greater challenges, and traditional frequency control methods are no longer effective in dealing with frequency fluctuations. Therefore, this paper proposes a frequency safety multi-level warning technology based on deep learning, which achieves accurate frequency prediction by combining convolutional neural networks and long short-term memory networks, and provides timely response through a multi-level warning mechanism. Experimental verification shows that this technology can effectively predict frequency fluctuations and take corresponding measures to ensure stable operation of the power system.