基于CNN的平波电抗器声纹模式识别方法

A voiceprint pattern recognition method of smoothing reactor based on CNN

为实现对平波电抗器运行状态的准确识别,引入一种基于CNN(卷积神经网络)的深度学习方法,建立了使用Mel时频谱的电抗器绕组声纹模式识别模型。以干式平波电抗器作为实验对象采集声音信号,使用Mel滤波器方法将采集到的声音信号转化为时频谱图,以不同的工况类型作为数据集的标签,基于CNN算法识别不同信号所对应的工况类型。结果表明,CNN可用于干式平波电抗器声纹模式的准确识别,优化后的神经网络对正弦激励、谐波激励和直流偏磁激励下的声纹信号识别准确率高达98.4%。研究结果为实现电网信号的智能化检测提供了潜在的技术方案。

In order to accurately identify the operating condition of the smoothing reactor,a deep learning method based on CNN(convolutional neural network)is introduced.A voiceprint pattern recognition model for reactor windings using Mel spectrogram is developed.The sound signals are collected using dry smoothing reactors as the experimental object.The Mel filter method is used to convert the collected sound signals into a spectrogram with different working conditions used as the labels of the data set.The CNN algorithm is used to identify the working conditions corresponding to the different signals.The results show that CNN can be used to accurately identify dry voiceprint patterns of smoothing reactors.The optimized neural network can achieve an accuracy of 98.4%in recognition of voiceprint signals under sinusoidal excitation,harmonic excitation and DC bias excitation.The research results provide a potential technical solution for realizing intelligent detection of power grid signals.