基于图像融合和双通道卷积神经网络的配电网故障选线方法研究

Research on distribution network fault line selection method based on image fusion and dual-channel convolutional neural network

针对传统的配电网故障选线方法受限于单一的故障诊断模型,提出一种基于图像融合和双通道卷积神经网络的配电网故障选线方法。研究目的是解决现有方法在面对高阻接地、噪声干扰、分布式电源接地、采样时间不同步等复杂工况时的准确性问题。首先,利用格拉姆角和场和格拉姆角差场将零序电流信号转成易于区分故障的二维图像,为图像处理提供了基础。其次,通过图像融合技术将GASF图像和GADF图像进行空间域图像融合,得到一张综合特征图像,充分利用了不同图像的特征,提高了特征表达的丰富性和有效性。接着,构建双通道卷积神经网络模型,其中一维卷积神经网络和ResNet50网络分别用于挖掘零序电流信号和格拉姆角场图像的特征。这种设计充分发挥了不同卷积神经网络在处理一维信号和二维图像时的优势。最后,将融合后的特征输入到Sigmoid函数实现故障线路的筛选。实验结果表明,该方法在各种复杂工况下的表现均优于传统方法,其准确率、Kappa系数、马修斯相关系数、召回率分别达到了99.97%、0.9993、0.9993、0.9995。这些结果表明,该方法不仅具有较高的准确性,还具有良好的鲁棒性和稳定性,能够有效应对高阻接地、噪声干扰、分布式电源接地和采样时间不同步等实际应用中的挑战。提出的方法为配电网故障选线提供了一种新颖且高效的解决方案,具有重要的实际应用价值和广泛的推广前景。

To address the limitations of traditional distribution network fault location methods,which rely on a single fault diagnosis model,a new fault location method for distribution networks based on image fusion and dual-channel convolutional neural networks is proposed.The aim of this study is to improve the accuracy of existing methods under complex conditions such as high-resistance grounding,noise interference,distributed power supply grounding,and unsynchronized sampling times.First,the zero-sequence current signals are converted into two-dimensional images using Gramian angular summation field(GASF)and Gramian angular difference field(GADF)techniques,providing a basis for image processing.Next,image fusion technology is employed to spatially fuse the GASF and GADF images,resulting in a comprehensive feature image that fully leverages the characteristics of different images,thereby enhancing the richness and effectiveness of feature representation.Subsequently,a dual-channel convolutional neural network model is constructed,where a one-dimensional convolutional neural network and a ResNet50 network are used to extract features from zero-sequence current signals and Gramian angular field images,respectively.This design takes full advantage of the strengths of different convolutional neural networks in processing one-dimensional signals and two-dimensional images.Finally,the fused features are input into a Sigmoid function to achieve fault line selection.Experimental results show that this method outperforms traditional methods under various complex conditions,with an accuracy rate,Kappa coefficient,Matthews correlation coefficient,and recall rate of 99.97%,0.9993,0.9993,and 0.9995,respectively.These results indicate that the proposed method not only has high accuracy but also exhibits good robustness and stability,effectively addressing challenges such as high-resistance grounding,noise interference,distributed power supply grounding,and unsynchronized sampling times in practical applications.The proposed method provides a novel and efficient solution for fault location in distribution networks,with significant practical application value and broad prospects for promotion.