Considering the spectral characteristics of substation noise,an Enhanced Generative Fixed-Filter Active Noise Control (EGFANC) approach is introduced to address the problems of slow convergence speed,weak tracking capability,and large computational complexity that perplexed adaptive algorithms.A lightweight one-Dimensional Convolutional Neural Network (1D CNN) is employed to output the weight vector based on noise frame information,then the weight vector is combined with sub-control filters to adaptively generate suitable control filters for various types of noise.The simulation results demonstrate that the EGFANC approach has superior noise reduction performance and robustness when dealing with dynamic noise and transformer harmonic noise.In addition,the proposed EGFANC approach can significantly reduce convergence time by selecting appropriate pre-trained control filters for different types of noise.