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

Abstract

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 .

Partial Discharge Pattern Recognition Based on an Ensembled Simple Convolutional Neural Network and a Quadratic Support Vector Machine

“…The resultant accuracy verified that IWOA had a good effect on the parameter optimization of SVMs. Sun et al also proposed an improved northern goshawk optimization (SCNGO) to optimize the parameter penalty factor and the kernel parameter of the SVM [7]. Fujioka et al utilized the maximum intensity observed in the PRPD pattern as the input data of an ANN [8].…”