Short text data model of secondary equipment faults in power grids based on LDA topic model and convolutional neural network
Author:
Affiliation:
1. Electric Power Research Institute, State Grid Xinjiang Electric Power Co., Ltd.,Urumuqi,China,830011
2. Nanjing SP-NICE Technology, Development CO.,Ltd.,Nanjing,China,210000
Funder
Science and Technology Project of State Grid
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9337559/9337493/09337597.pdf?arnumber=9337597
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
1. Topic Mining and Hotspot Perspective on Nuclear Power Generation Research in China;2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC);2024-06-07
2. Overhead transmission line condition assessment based on intention classification and slot filling using optimized BERT model;Energy Reports;2023-09
3. Technological forecasting based on estimation of word embedding matrix using LSTM networks;Technological Forecasting and Social Change;2023-06
4. Research on Construction of Dispatching Knowledge Model of Power Grid;2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE);2022-04-22
5. Short Text Classification for Faults Information of Secondary Equipment Based on Convolutional Neural Networks;Energies;2022-03-24
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