电测与仪表2024，Vol.61Issue(1)：83-90,98,9.DOI:10.19753/j.issn1001-1390.2024.01.013
基于注意力机制优化组合神经网络的电力缺陷等级确定方法
A determination method of defect grades in electrical equipment based on combination neural network optimized by attention mechanism
摘要
Abstract
In order to solve the problem that the accuracy of word segmentation in power defect descriptions is not good and the single neural network model has its own shortcomings,a determination method of defect grades in electrical equip-ment based on combination neural network optimized by attention mechanism is proposed in this paper.The distributed character granularity vector is used for representation of power defect descriptions.The local features and sequence fea-tures of power defect descriptions are extracted by using the convolutional recurrent neural network which is composed by convolutional neural network and bidirectional long short-term memory network.The attention mechanism is used to assign weights of the semantic features obtained by the combination neural network,so as to reduce the loss of key features and further enhance the influence of key information on the classification results.Taking 110 000 defect description data of Yunnan Power Grid Company from 2014 to 2019 as experimental objects,the Acc,MF1 and WF1 values of the method pro-posed in this paper are 0.927 5,0.911 2 and 0.927 5,which illustrates that the proposed method is effective and feasible in the determination of the power defect grades,and provides help for intelligent operation of power grid.关键词
卷积循环神经网络/字粒度/注意力机制/电力缺陷描述/状态评价Key words
convolutional recurrent neural network/character granularity/attention mechanism/power defect descrip-tions/condition assessment分类
信息技术与安全科学引用本文复制引用
程宏伟,高莲,于虹,李鹏..基于注意力机制优化组合神经网络的电力缺陷等级确定方法[J].电测与仪表,2024,61(1):83-90,98,9.基金项目
国家自然科学基金资助项目(61763049) （61763049）
云南省应用基础研究计划重点项目(2018FA032) （2018FA032）
