摘要
输电线路的故障会给电力系统带来重大影响,因此对其进行准确预测变得尤为重要,文章旨在探索如何有效地结合现有输电线路监控系统与先进的深度学习技术,以实现对故障的预测。本研究选择了基于MobileNet架构的卷积神经网络,采用了深度分离卷积、Transformer注意力机制、多尺度特征提取等技术,训练过程中还采用了迁移学习和域自适应技术来增强模型的泛化能力。在验证数据集上,本研究算法实现了更短的检测时间和更高的正确率,优于VGG16和原卷积神经网络,也证明了模型在各种场景下的鲁棒性。本次探索为输电线路的维护和预测提供了可行的方法,也为电力行业在实际应用中集成深度学习技术提供参考。
Faults in transmission lines can have significant impacts on the power system,making accurate prediction of such faults particularly crucial.This paper aims to explore how to effectively integrate existing transmission line monitoring systems with advanced deep learning technologies to develop fault prediction methods.This research adopted a convolutional neural network based on the MobileNet architecture,utilizing techniques such as depthwise separable convolutions,Transformer attention mechanisms,and multi-scale feature extraction.Additionally,transfer learning and domain adaptation techniques were employed during the training process to enhance the model's generalization capabilities.On the validation dataset,the proposed algorithm achieved shorter detection times and higher accuracy rates,outperforming both VGG16 and traditional convolutional neural networks.This also demonstrated the robustness of the model across various scenarios.
作者
茹洁宣
RU Jiexuan(Beijing Bowang Huake Science and Technology Co.,Ltd.,Beijing Fengtai 100071,China)
出处
《农村电气化》
2024年第2期1-5,共5页
Rural Electrification
关键词
深度学习
卷积神经网络
输电线路
故障预测
deep learning
convolutional neural networks
transmission lines
fault prediction
