Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11): 292-297.doi: 10.11896/jsjkx.230500096

• Artificial Intelligence • Previous Articles     Next Articles

Equipment Anomaly Diagnosis Based on DGA and Sparse Support Vector Machine

PAN Lianrong1, ZHANG Fuquan2, HE Jinglong1, YANG Jiayi1   

  1. 1 Power Dispatching Control Center,Guangxi Power Grid Co. LTD.,Nanning 530023,China
    2 School of Computer Science,Beijing Institute of Technology,Beijing 100081,China
  • Received:2023-05-15 Revised:2023-08-28 Online:2024-11-15 Published:2024-11-06
  • About author:PAN Lianrong,born in 1985.His main research interests include deep lear-ning,data processing and device anomaly analysis.
  • Supported by:
    General Project of the National Natural Science Foundation of China(61871204),Department of Science and Technology Fujian Province Guided Project(2018H0028) and Science and Technology Project of Guangxi Power Grid in 2023(046000KK52222021).

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Abstract: In order to effectively improve the accuracy and efficiency of equipment anomaly diagnosis based on machine learning,a fault diagnosis model based on sparse support vector machine is proposed.Firstly,the principle of abnormal diagnosis and cha-racteristic gas are analyzed,and the relationship between fault types and characteristic gas is given.Secondly,the data is preprocessed from 4 aspects,including cleaning,normalization,balance and division.Then,in order to solve the problem of sparsity of least squares support vector machine,a method is proposed to map data samples to a high-dimensional kernel space,and cluster the mapped data in kernel space distance by spectral clustering algorithm,to realize the data preprocessing of least squares support vector machine,so as to realize its sparseness.Finally,the specific experimental analysis is carried out on a small sample dataset.The results show that,for 9 types of faults,compared with other diagnosis models based on different types of support vector machines,the proposed diagnosis model only needs 11 iterations to obtain the maximum fitness value,and the average diagnosis accuracy rate is 96.67%,with higher accuracy and efficiency.

Key words: Anomaly diagnosis, Machine learning, Least square support vector machine, Analysis of dissolved gas in oil, Rarefaction

CLC Number: 

  • TP391
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