基于卷积神经网络的电流互感器畸变信号识别方法

A Distortion Signal Recognition Method for Current Transformer Based on Convolutional Neural Network

在识别电流互感器畸变信号时,主要通过基础的神经网络提取信号特征,只能得到低层特征,使得畸变信号识别结果而F1分数较低。因此,应用卷积神经网络,设计一种新型电流互感器畸变信号识别方法。明确电流互感器的工作原理,绘制整体高频等效电路图,并基于此建立畸变信号模型。应用软阈值去噪原理,对采集的电流互感器信号进行去噪处理。再依托于多通道卷积神经网络,设计信号特征提取模型结构,将去噪后的信号输入其中进行深度学习,组合每个通道输出的低层特征,输出更加抽象的高层信号特征。最后针对特征提取进一步计算,构建特征空间,以此来实现畸变信号的准确识别。实验结果表明:所提方法识别结果的F1分数保持在0.97以上,展现出极好的信号识别效果。

When identifying current transformer distorted signals,the basic neural network is mainly used to extract signal features,which can only obtain low-level features,resulting in a low F1 score for distorted signal recognition.Therefore,using convolutional neural networks,a novel method for identifying current transformer distortion signals is designed.Define the working principle of the current transformer,draw the overall high-frequency equivalent circuit diagram,and establish a distortion signal model based on this.The principle of soft threshold denoising is applied to denoise the collected current transformer signal.Relying on multi-channel convolutional neural networks,a signal feature extraction model structure is designed.The denoised signal is inputted into it for deep learning,and the low-level features outputted from each channel are combined to output more abstract high-level signal features.Finally,further calculation is conducted for feature extraction to construct a feature space to achieve accurate recognition of distorted signals.Experimental results show that the F1 score of the proposed method’s recognition results remains above 0.97,exhibiting excellent signal recognition effects.