电机与控制应用2024，Vol.51Issue(2)：34-43,10.DOI:10.12177/emca.2023.179
基于二维特征提取方法与混合神经网络的接触式采集110kV三相三绕组变压器无载调压异常放电声纹的识别方法
A Recognition Method Based on Two-Dimensional Voiceprint Feature Extraction Method and Hybrid Neural Network for Contact-Collected Abnormal Discharge Voiceprint of 110 kV Three-Phase Three-Winding Power Transformer in No-Load Voltage Regulation
摘要
Abstract
Abnormal discharge is a dangerous power transformer fault,which can lead to serious safety hazards if not detected in time.A method for identifying abnormal discharge in transformer is proposed by collecting the voiceprint signal in the transformer box through the contact voice pickup.And a feature extraction method and a deep neural network structure are proposed to achieve efficient identification of abnormal transformer discharge.Firstly,a two-dimensional voiceprint feature extraction method combining Mel frequency and key frequency is designed.Then,a hybrid two-dimensional feature recognition model based on convolutional neural network and Transformer network is used to accurately identify the abnormal discharge voiceprint signal while ensuring the speed.Finally,according to the experimental analysis of the discharge data collected from the 110 kV three-phase three-winding transformer in the no-load voltage regulation process,the recognition speed of the proposed method increases by 0.19 seconds per sample,and the accuracy increases by 4.5％compared with ResNet50.关键词
变压器异常放电/声纹识别/声纹特征提取/混合神经网络Key words
abnormal ischarge of transformer/voiceprint分类
信息技术与安全科学引用本文复制引用
童旸,黄文礼,李磊,晏雨晴..基于二维特征提取方法与混合神经网络的接触式采集110kV三相三绕组变压器无载调压异常放电声纹的识别方法[J].电机与控制应用,2024,51(2):34-43,10.基金项目
新型电力系统智能运维安徽省联合共建学科重点实验室成果 Results of the Anhui Provincial Key Laboratory of Joint Co-construction of New Power System Intelligent Operation and Maintenance （）
