南京信息工程大学学报2025，Vol.17Issue(2)：293-300,8.DOI:10.13878/j.cnki.jnuist.20240505001
改进生成式固定滤波器变电站噪声有源控制
Enhanced generative fixed-filters for active control of substation noise
摘要
Abstract
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 perform-ance and robustness when dealing with dynamic noise and transformer harmonic noise.In addition,the proposed EG-FANC approach can significantly reduce convergence time by selecting appropriate pre-trained control filters for dif-ferent types of noise.关键词
有源噪声控制/生成式固定滤波器/卷积神经网络/深度学习Key words
active noise control(ANC)/generative fixed-filters/convolutional neural network(CNN)/deep learn-ing分类
通用工业技术引用本文复制引用
费彬,沈海平,阙云飞,从乐瑶,蒋逸文..改进生成式固定滤波器变电站噪声有源控制[J].南京信息工程大学学报,2025,17(2):293-300,8.基金项目
国网江苏省电力有限公司科技项目(J2023097) （J2023097）
