电力设备故障声学定位面临严重的背景噪声干扰,导致波达方向(direction of arrival,DOA)估计精度和实时性不佳,故障定位性能受限.对此,提出一种基于匹配滤波器和深度神经网络(match filter-deep neural networks,MF-DNN)的DOA估计技术,在低信噪比情况下,实现基于声学检测的高速电力设备故障声学定位.该方法通过匹配滤波器提高波达信号的信噪比,提取上三角元素特征降低了特征维度,采用自适应矩估计算法优化网络更新策略,降低了网络规模,最终实现低信噪比条件下对特征目标的高速DOA估计.实验结果表明,该方法在低信噪比下的估计速度和估计精度均优于传统算法.
Fault Location Method of Power Equipment Based on MF-DNN Algorithm Using Acoustic Signal
When using acoustic signals to locate faults in the power equipment,the system often experiences severe background noise interference,resulting in low accuracy of direction of arrival(DOA)estimation and slow localization speed.Therefore,this paper proposes a DOA estimation technique based on matched filters and deep neural networks(MF-DNN).When the signal to noise ratio(SNR)of acoustic signals is very low,this method achieves high-speed acoustic localization of power equipment faults.The method improves SNR of the arrival signal through a matched filter module and reduces the feature dimension by extracting the features of the upper triangle elements.It optimizes the network update strategy through the adaptive moment estimation(Adam)algorithm,reduces the network size,and ultimately achieves high-speed DOA estimation of feature targets under low SNR conditions.The experimental results show that the estimation speed and accuracy of this method are superior to traditional algorithms under low SNR acoustic signals.
fault locationdirection of arrivalDOAmatched filterdeep neural network(DNN)low signal to noise ratio
万方数据
维普
