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.
Highlights
HVDC (High Voltage Direct Current) transmission has the advantages of low line cost, not being restricted by synchronous stability problems, the project of DC cable has developed rapidly in recent years [1,2,3]
We compare adaptive moment estimation (ADAM)-deep belief network (DBN) with naïve Bayes (NB), K-nearest neighbor (KNN), support vector with naï ve Bayes (NB),(BPNN)
A partial discharge (PD) pattern recognition method of DC XLPE cable based on DBN algorithm is proposed to distinguish the different types of defect
Summary
HVDC (High Voltage Direct Current) transmission has the advantages of low line cost, not being restricted by synchronous stability problems, the project of DC cable has developed rapidly in recent years [1,2,3]. Based on features extracted from TRPD, the usage of neural networks, support vector machines, and other algorithms takes a lot of time to study how to extract features efficiently. Wang applies the DBN to the hybrid fault diagnosis method in transformer oil [17], and Zhang uses DBN to identify the analog circuit incipient faults [18] Both of them have good performance and prove that the deep learning method has strong characteristic learning ability based on the original data. A modified DBN algorithm based on DC PD pulse waveforms is proposed to achieve the pattern recognition of DC cable insulation defects. The experimental results show that the proposed method improves the accuracy of the recognition of DC XLPE cable insulation defects
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