Short Text Classification for Faults Information of Secondary Equipment Based on Convolutional Neural Networks

Jiufu Liu, Hongzhong Ma, Xiaolei Xie and Jun Cheng
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Jiufu Liu: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Hongzhong Ma: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Xiaolei Xie: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Jun Cheng: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China

Energies, 2022, vol. 15, issue 7, 1-15

Abstract: As the construction of smart grids is in full swing, the number of secondary equipment is also increasing, resulting in an explosive growth of power big data, which is related to the safe and stable operation of power systems. During the operation of the secondary equipment, a large amount of short text data of faults and defects are accumulated, and they are often manually recorded by transportation inspection personnel to complete the classification of defects. Therefore, an automatic text classification based on convolutional neural networks (CNN) is proposed in this paper. Firstly, the topic model is used to mine the global features. At the same time, the word2vec word vector model is used to mine the contextual semantic features of words. Then, the improved LDA topic word vector and word2vec word vector are combined to absorb their respective advantages and utilizations. Finally, the validity and accuracy of the model is verified using actual operational data from the northwest power grid as case study.

Keywords: secondary equipment; CNN; short text classification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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