Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBN

Zhe Li, Yongpeng Xu and Xiuchen Jiang
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Zhe Li: Academy of Information Technology and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Yongpeng Xu: Academy of Information Technology and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Xiuchen Jiang: Academy of Information Technology and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Energies, 2020, vol. 13, issue 17, 1-12

Abstract: Pattern recognition of DC partial discharge (PD) receives plenty of attention and recent researches mainly focus on the static characteristics of PD signals. In order to improve the recognition accuracy of DC cable and extract information from PD waveforms, a modified deep belief network (DBN) supervised fine-tuned by the adaptive moment estimation (ADAM) algorithm is proposed to recognize the four typical insulation defects of DC cable according to the PD pulse waveforms. Moreover, the effect of the training sample set size on recognition accuracy is analyzed. Compared with naive Bayes (NB), K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural networks (BPNN), the ADAM-DBN method has higher accuracy on four different defect types due to the excellent ability in terms of the feature extraction of PD pulse waveforms. Moreover, the increase of training sample set size would lead to the increase of recognition accuracy within a certain range.

Keywords: DC cross linked polyethylene (XLPE) cable; partial discharge (PD); restricted Boltzmann machines (RBM); deep belief network (DBN); adaptive moment estimation (ADAM) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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