Abstract
Abstract
Fault diagnosis of power equipment is extremely crucial to the stability of power grid systems. However, complex operating environments, high costs and limitations of single-modal signals are the biggest bottlenecks. To this end,a multi-tream, multi-scale lightweight Swin multilayer perceptron (MLP) network (MLSNet) with an adaptive channel-spatial soft threshold is proposed in this paper. First, a Res2net-based feature-enhanced method is used to learn the correlated features of vibration and voltage multi-modal signals. Second, a novel MLSNet is designed to combine the benefits of Swin transformers with an MLP with a lightweight convolutional neural network and employs a staged model to extract various scale features. Third, an adaptive deep fusion approach employing a channel-spatial soft threshold module is used to integrate and recalibrate staged information at different scales. The overall accuracy of the proposed method can reach 98.73% in various experiments, potentially making it an effective method for online fault diagnosis of power transformers.
Funder
National Natural Science Foundation of China
National Key Research and Development plan both “smart grid technology and equipment”
Wuhan Science and Technology Plan Project
Hubei Province Key Research and Development Plan
Important Scientific Instruments and Equipment Development
Fundamental Research Funds for the Central Universities
State Key Program of National Natural Science Foundation of China
Equipment research project in advance
Subject
Applied Mathematics,Instrumentation,Engineering (miscellaneous)
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献