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Open AccessArticle

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

College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China
State Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 211113, China
School of Electrical Engineering, Southeast University, Nanjing 210096, China
Author to whom correspondence should be addressed.
Energies 2019, 12(1), 43;
Received: 22 November 2018 / Revised: 14 December 2018 / Accepted: 21 December 2018 / Published: 24 December 2018
(This article belongs to the Special Issue Power Quality: Monitoring, Mitigation, and New Types of Disturbances)

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. View Full-Text
Keywords: online recognition; voltage sag; deep belief network online recognition; voltage sag; deep belief network
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MDPI and ACS Style

Mei, F.; Ren, Y.; Wu, Q.; Zhang, C.; Pan, Y.; Sha, H.; Zheng, J. Online Recognition Method for Voltage Sags Based on a Deep Belief Network. Energies 2019, 12, 43.

AMA Style

Mei F, Ren Y, Wu Q, Zhang C, Pan Y, Sha H, Zheng J. Online Recognition Method for Voltage Sags Based on a Deep Belief Network. Energies. 2019; 12(1):43.

Chicago/Turabian Style

Mei, Fei; Ren, Yong; Wu, Qingliang; Zhang, Chenyu; Pan, Yi; Sha, Haoyuan; Zheng, Jianyong. 2019. "Online Recognition Method for Voltage Sags Based on a Deep Belief Network" Energies 12, no. 1: 43.

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