Residual current fault type recognition based on S3VM and KNN cooperative training

Xiangke Zhang; Yajing Wang; Zhenhai Dou; Wei Wang; Yunpeng Bai

Residual current fault type recognition based on S3VM and KNN cooperative training

Vol. 22, No. 11, pp. 1966-1977, Nov. 2022

10.1007/s43236-022-00497-9

residual current

Fault type recognition

S3VM

KNN

Abstract

It is difficult to detect the residual current of specifi c fault types in low-voltage distribution networks, which results in few labeled residual current samples. Thus, it is difficult to recognize the fault types of residual current. To solve this problem, a cooperative training classification model based on an improved squirrel search algorithm (ISSA) for a semi-supervised support vector machine (S3VM) and the k-nearest neighbor (KNN) is proposed (ISSA-S3VM–KNN). First, the residual current is decomposed into k intrinsic mode functions (IMFs) by variational mode decomposition (VMD), and the characteristic parameters of the IMFs are extracted to obtain a characteristic dataset for establishing a classifi cation model. Second, to solve the problem where it is difficult to the select parameters (such as the penalty factors, slack variables and kernel function) of a S3VM, an ISSA parameter optimization method is proposed to self-adaptively select the optimal combination of parameters for the S3VM. Finally, the KNN is used to verify the classification results of an ISSA-S3VM through cooperative training, which further improves the classification accuracy of the S3VM for unlabeled residual current samples. Classifi cation results of measured and simulation data show that the classification accuracy of the ISSA-S3VM–KNN is higher than that of the SVM–BPNN, WE–AE–BPNN, and PSO–SVM. The ISSA-S3VM–KNN provides a certain theoretical basis for achieving fast and accurate residual current fault type recognition.

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Cite this article

[IEEE Style]

X. Zhang, Y. Wang, Z. Dou, W. Wang, Y. Bai, "Residual current fault type recognition based on S3VM and KNN cooperative training," Journal of Power Electronics, vol. 22, no. 11, pp. 1966-1977, 2022. DOI: 10.1007/s43236-022-00497-9.

[ACM Style]

Xiangke Zhang, Yajing Wang, Zhenhai Dou, Wei Wang, and Yunpeng Bai. 2022. Residual current fault type recognition based on S3VM and KNN cooperative training. Journal of Power Electronics, 22, 11, (2022), 1966-1977. DOI: 10.1007/s43236-022-00497-9.