Status Assessment of Secondary Equipment in Substation Based on Fuzzy Comprehensive Support Vector Machine Method

Ma, Binghe; Dong, Hongbiao

doi:10.11591/telkomnika.v12i2.4116

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Reference [3] puts forward an optimal time-frequency analysis method to analyze the difficulties of electrical characteristics analysis, and puts forward the corresponding intelligent substation monitoring method. In recent years, with the gradual development of artificial intelligence algorithms in various industries, various artificial intelligence algorithms (such as support vector machine [9][10][11] , artificial neural network [12][13] , etc.) have been applied to secondary equipment condition monitoring, and achieved certain results in practical application.…”

Section: Introductionmentioning

confidence: 99%

State monitoring method of automation equipment in independent and controllable substation based on grey prediction model

Ruan,

Cui,

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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Section: Introductionmentioning

confidence: 99%

State monitoring method of automation equipment in independent and controllable substation based on grey prediction model

Ruan,

Cui,

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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“…Machine learning methods based on statistics mainly include support vector machine (SVM), K-means clustering, decision tree, Apriori and so on [13]. For example, in [14], a secondary device state evaluation model based on fuzzy comprehensive SVM was proposed, which improved the traditional SVM and corrected the errors caused by subjective factors. In [15], based on the historical defect data of a secondary device in smart substation, a method of mining and analyzing the defect data of secondary device based on the Apriori algorithm was proposed, which provided reference for equipment operation and control.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Dynamic condition vector assessment method for electric power equipment of overall process: Novel and necessary technical approach of condition-based-assessment

Liu¹,

Qi²,

Wu³

et al. 2017

2017 3rd International Conference on Condition Assessment Techniques in Electrical Systems (CATCON)

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Status Assessment of Secondary Equipment in Substation Based on Fuzzy Comprehensive Support Vector Machine Method

Cited by 3 publications

References 4 publications

State monitoring method of automation equipment in independent and controllable substation based on grey prediction model

State monitoring method of automation equipment in independent and controllable substation based on grey prediction model

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

Dynamic condition vector assessment method for electric power equipment of overall process: Novel and necessary technical approach of condition-based-assessment

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