Considering the spectral characteristics of substation noise, an enhanced generative fixed filter active noise control (Enhanced Generative Fixed Filter Active Noise Control, EGFANC) method was introduced to improve the limitations of adaptive algorithms such as slow convergence speed, weak tracking ability, and large computational complexity. A lightweight one-dimensional convolutional neural network (1D CNN) was used to output the weight vector based on noise frame information, then the weight vector was combined with sub control filters to adaptively generate suitable control filters for various types of noise. The simulation results show that the EGFANC method has better noise reduction performance and robustness in dealing with dynamic noise and transformer harmonic noise. In addition, EGFANC can significantly reduce convergence time by selecting appropriate pre trained control filters for different types of noise.