Online Recognition Method for Voltage Sags Based on a Deep Belief Network

Fei Mei, Yong Ren, Qingliang Wu, Chenyu Zhang, Yi Pan, Haoyuan Sha and Jianyong Zheng
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Fei Mei: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Yong Ren: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Qingliang Wu: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Chenyu Zhang: State Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 211113, China
Yi Pan: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Haoyuan Sha: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Jianyong Zheng: School of Electrical Engineering, Southeast University, Nanjing 210096, China

Energies, 2018, vol. 12, issue 1, 1-16

Abstract: Voltage sag is a serious power quality phenomenon that threatens industrial manufacturing and residential electricity. A large-scale monitoring system has been established and continually improved to detect and record voltage sag events. However, the inefficient process of data sampling cannot provide valuable information early enough for governance of the system. Therefore, a novel online recognition method for voltage sags is proposed. The main contributions of this paper include: 1) The causes and waveform characters of voltage sags were analyzed; 2) according to the characters of different sag waveforms, 10 voltage sag characteristic parameters were proposed and proven to be effective; 3) a deep belief network (DBN) model was built using these parameters to complete automatic recognition of the sag event types. Experiments were conducted using voltage sag data from one month recorded by the 10 kV monitoring points in Suqian, Jiangsu Province, China. The results showed good performance of the proposed method: Recognition accuracy was 96.92%. The test results from the proposed method were compared to the results from support vector machine (SVM) recognition methods. The proposed method was shown to outperform SVM.

Keywords: online recognition; voltage sag; deep belief network (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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