摘要
输电设备配有振动信号测量系统;在不同机械与电气故障状况下的振动特性,可分析OLTC状况与振动信号之间的关系。但是,低温环境会导致设备材料的脆化及润滑剂凝固,改变设备振动和声纹特征,增加异常振动声纹识别的复杂性。提出一种寒冷环境下的输电设备复杂异常振动的声纹识别方法。将经验模态分解(Empirical Mode Decomposition, EMD)和斯坦无偏估计(Stein's unbiased estimate, SURE)相结合,采集输变电设备的振动声纹信号,并对其进行去噪处理;将去噪处理后的声纹信号划分为多个声纹片段,并转换为语谱图。在特征提取阶段,将正常语谱图作为输入,利用长短期记忆网络(Long Short-Term Memory, LSTM)进行训练,以分类输入的输变电设备语谱图声纹样本,并确定异常样本,实现对输变电设备异常振动声纹的准确识别。实验结果表明:所提方法可以精准识别输变电设备异常振动声纹,识别率均在96%以上,识别耗时94.47 ms。
The transmission equipment is equipped with a vibration signal measurement system;The vibration characteristics under different mechanical and electrical fault conditions can be analyzed to determine the relationship between OLTC conditions and vibration signals.However,low-temperature environments can lead to embrittlement of equipment materials and solidification of lubricants,changing the vibration and voiceprint characteristics of the equipment,and increasing the complexity of identifying abnormal vibration voiceprints.Propose a voiceprint recognition method for complex abnormal vibrations of transmission equipment in cold environments.Combining Empirical Mode Decomposition(EMD)with Stein's unbiased estimate(SURE)to collect vibration voiceprint signals of power transmission and transformation equipment,and denoise them;Divide the denoised voiceprint signal into multiple voiceprint segments and convert them into spectrograms.In the feature extraction stage,the normal spectrogram is used as input,and a Long Short Term Memory(LSTM)network is used for training to classify the input spectrogram voiceprint samples of power transmission and transformation equipment,and determine abnormal samples to achieve accurate recognition of abnormal vibration voiceprint of power transmission and transformation equipment.The experimental results show that the proposed method can accurately identify abnormal vibration voiceprints of power transmission and transformation equipment,with recognition rates of over 96%and recognition time of 94.47 ms.
作者
张德文
张大宁
王磊
郭跃男
ZHANG Dewen;ZHANG Daning;WANG Lei;GUO Yuenan(State Grid Heilongjiang Electric Power Co.,Ltd.,Electric Power Science Research Institute,Haerbin 150030,China;Southwest Jiaotong University,Xi’an 710061 China;State Grid Heilongjiang Electric Power Co.,Ltd.,Headquarters,Haerbin 150000,China)
出处
《自动化与仪器仪表》
2024年第7期192-195,共4页
Automation & Instrumentation
基金
寒冷地区自适应智能红外测试终端的研发及应用研究(52243723000Q)。
关键词
寒冷环境
输变电设备
异常振动
声纹识别
cold environment
power transmission and transformation equipment
abnormal vibration
voiceprint recognition
