基于T-DSCNN的故障选线方法研究
T-DSCNN-based Faulty Line Determination
鲁玉海1
作者信息
- 1. 中国电建集团河南省电力勘测设计院有限公司,河南郑州 450000
- 折叠
摘要
研究了一种基于迁移深度可分离卷积神经网络(T-DSCNN)的故障选线方法,旨在提高电力系统中故障选线的准确性和效率.通过引入迁移学习的概念,T-DSCNN能够利用预训练的模型参数作为初始权重,加速模型的训练过程并提高其泛化能力.深度可分离卷积技术的应用减少了模型的参数量,降低了计算复杂度,从而使得模型在保持高准确率的同时更适用于实时故障选线的应用场景.在基于标准数据集的故障选线测试中,T-DSCNN表现出了优异的性能,识别速度和准确率高于传统卷积神经网络和其他故障选线方法.
Abstract
In this paper,a faulty line determination method based on transfer-depthwise separable convolution neural net-works(T-DSCNN)is studied.The method aims to improve the accuracy and efficiency of faulty line determination in power systems.By introducing the concept of migration learning,the T-DSCNN is able to accelerate the training process of the model and improve its generalisation ability by using the pre-trained model parameters as initial weights.Moreover,the use of deep separable convolution technique reduces the number of parameters of the model and lowers the computa-tional complexity,thus making the model more suitable for the scenario of real-time determining faulty line while maintai-ning high accuracy.Through testing on standard datasets,T-DSCNN shows excellent performance on the fault routing task,which significantly improves the recognition speed and accuracy compared to conventional convolutional neural net-works and other methods.
关键词
T-DSCNN/故障选线/迁移学习/融合图像Key words
T-DSCNN/faulty line determination/migration learning/fused image引用本文复制引用
出版年
2024
NETL
NSTL
万方数据