摘要
在电力电缆故障精确定位中声磁同步法由于具有精度高与抗干扰能力强的优点而得到广泛的应用,但放电声音信号的有效检测是其难点。由于小波包变换在检测正常信号中是否含有瞬态异常现象方面具有独特的优势,自适应滤波器具有对信号和噪声的先验知识需求少的特性以及遗传算法具有不依赖于具体问题的优点,提出了一种基于小波包变换分解信号、自适应滤波估计噪声与遗传算法寻优重构相结合的声音信号增强算法。实验研究表明,该算法精确性高、鲁棒性强,尤其适用于电缆故障点放电声不明显时声音信号提取的情况,从而解决了电缆故障精确定位中对背景噪声要求高、识别范围小的问题。
There are two advantages including high accuracy and strong free-interference for the magneto-acoustic synchronous method,which is a fine location method and is widely used. However,it is difficult to effectively detect the audio signal. Because the wavelet packet transform in detecting whether the normal signal contains a anomaly transient or not,an audio signal enhancement algorithm based on wavelet packet transform decomposing signal,adaptive filter estimating noise and genetic algorithm optimizing signal combined reconstructing signal is presented. The adaptive filter needs less for prior knowledge of the signal and noise,and genetic algorithm does not depend on the specific problems. Experimental results validate its high accuracy and strong robustness. In particular,it can be applied to the extracting audio signal under the condition that the cable fault discharge is weak. Therefore,it is a potential eligible solution to strict constraint of the background noise and narrow recognition range in pinpointing of cable failure location.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2017年第1期17-22,共6页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金重点项目(51490660)
国家自然科学基金(51405362)项目资助
关键词
声音增强
小波包变换
自适应滤波
遗传算法
故障测距
audio enhancement
wavelet packet transform
adaptive filtering
genetic algorithm
fault location
