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

作　　者：费彬 沈海平 阙云飞 从乐瑶 蒋逸文 FEI Bin;SHEN Haiping;QUE Yunfei;CONG Leyao;JIANG Yiwen(Wuxi Power Supply Company of State Grid Jiangsu Electric Power Co.,Ltd.,Wuxi 214026,China)

机构地区：[1]国网江苏省电力有限公司无锡供电分公司,无锡214026

出　　处：《南京信息工程大学学报》2025年第2期293-300,共8页Journal of Nanjing University of Information Science & Technology

基　　金：国网江苏省电力有限公司科技项目(J2023097)。

摘　　要：考虑到变电站噪声的频谱特点,针对自适应算法存在收敛速度慢、跟踪能力弱和运算量大的缺陷,研究了一种改进生成式固定滤波器有源噪声控制(Enhanced Generative Fixed-Filter Active Noise Control,EGFANC)方法.采用轻量级的一维卷积神经网络(1-Dimensional Convolutional Neural Network,1D CNN)根据噪声帧信息输出权重向量后与子控制滤波器组合,以自适应地生成适用于各种噪声的控制滤波器.仿真结果表明,EGFANC方法在处理动态噪声和变压器谐波噪声时具有更好的降噪性能和鲁棒性,同时,EGFANC为不同类型噪声选择适当的预训练控制滤波器,可以显著减少收敛时间.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.

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 

分 类 号：TB535[理学—物理] TM41[理学—声学] TN713[电气工程—电器]