Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM

Kai Chen, Rabea Jamil Mahfoud, Yonghui Sun, Dongliang Nan, Kaike Wang, Hassan Haes Alhelou and Pierluigi Siano
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Kai Chen: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Rabea Jamil Mahfoud: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Yonghui Sun: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Dongliang Nan: School of Electrical Engineering, Xinjiang University, Urumqi 830047, China
Kaike Wang: Electric Power Research Institute, State Grid Electric Power Co., Ltd., Urumqi 830011, China
Hassan Haes Alhelou: Department of Electrical Power Engineering, Faculty of Mechanical and Electrical Engineering, Tishreen University, Lattakia 2230, Syria
Pierluigi Siano: Department of Management & Innovation Systems, University of Salerno, 84084 Salerno, Italy

Energies, 2020, vol. 13, issue 17, 1-17

Abstract: In the process of the operation and maintenance of secondary devices in smart substation, a wealth of defect texts containing the state information of the equipment is generated. Aiming to overcome the low efficiency and low accuracy problems of artificial power text classification and mining, combined with the characteristics of power equipment defect texts, a defect texts mining method for a secondary device in a smart substation is proposed, which integrates global vectors for word representation (GloVe) method and attention-based bidirectional long short-term memory (BiLSTM-Attention) method in one model. First, the characteristics of the defect texts are analyzed and preprocessed to improve the quality of the defect texts. Then, defect texts are segmented into words, and the words are mapped to the high-dimensional feature space based on the global vectors for word representation (GloVe) model to form distributed word vectors. Finally, a text classification model based on BiLSTM-Attention was proposed to classify the defect texts of a secondary device. Precision, Recall and F1-score are selected as evaluation indicators, and compared with traditional machine learning and deep learning models. The analysis of a case study shows that the BiLSTM-Attention model has better performance and can achieve the intelligent, accurate and efficient classification of secondary device defect texts. It can assist the operation and maintenance personnel to make scientific maintenance decisions on a secondary device and improve the level of intelligent management of equipment.

Keywords: secondary device; defect classification; GloVe; attention mechanism; text mining (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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