Application of SCNGO-VMD-SVM in Identification of Gas Insulated Switchgear Partial Discharge

Partial discharge (PD) is one of the main reasons of insulation deterioration in gas insulated switchgear (GIS). How to efficiently and accurately identify PD signals is an important guarantee for the stable operation of GIS. In this paper, an improved northern goshawk optimization (SCNGO) is propos...

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Bibliographic Details
Published in:IEEE Access
Main Authors: Wei Sun, Hongzhong Ma, Sihan Wang
Format: Article in Journal/Newspaper
Language:English
Published: IEEE 2024
Subjects:
Partial discharge
gas insulated switchgear
northern goshawk optimization
variational mode decomposition
support vector machine
fault identification
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Northern Goshawk
Online Access:https://doi.org/10.1109/ACCESS.2024.3377688
https://doaj.org/article/802c9ebe982043fbbc1bc4349213b2bf
Description
Summary:Partial discharge (PD) is one of the main reasons of insulation deterioration in gas insulated switchgear (GIS). How to efficiently and accurately identify PD signals is an important guarantee for the stable operation of GIS. In this paper, an improved northern goshawk optimization (SCNGO) is proposed, which automatically optimizes parameters of variational mode decomposition (VMD) and support vector machine (SVM) to realize fault identification of GIS PD. Firstly, to overcome the shortcomings that NGO is easy to fall into local optimal solution and slow convergence speed, the opposite learning of refraction strategy and sine cosine algorithm (SCA) are introduced to optimize NGO. By comparing the test functions of various algorithms, the superiority of SCNGO algorithm is proved. Then, GIS PD experiment is designed for fault signal acquisition and algorithm verification. SCNGO-VMD is used for parameter adaptive optimization of PD signals. On this basis, the effective intrinsic mode functions (IMFs) are screened by composite index. Furthermore, time-domain, frequency-domain, and entropy features are constructed as mixed features and t-SNE is used to reduce the dimension. Finally, the feature vectors are input to SCNGO-SVM for fault identification. Through experimental analysis, compared with other algorithms, the proposed algorithm model has good state identification accuracy for GIS PD fault diagnosis. The paper provides a reference for the application of optimization algorithm in GIS PD fault identification.

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