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

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...

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Bibliographic Details
Published in:Energies (Basel) 2022-04, Vol.15 (7), p.2400
Main Authors: Liu, Jiufu, Ma, Hongzhong, Xie, Xiaolei, Cheng, Jun
Format: Article
Language:English
Subjects:
Artificial intelligence
Classification
CNN
Data mining
Efficiency
Electric power systems
Fault diagnosis
Inspection
Machine learning
Model accuracy
Neural networks
secondary equipment
Semantics
short text classification
Text categorization
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en15072400