Self-Supervised Voltage Sag Source Identification Method Based on CNN

Danqi Li,Fei Mei,Jianyong Zheng,Chenyu Zhang,Haoyuan Sha

doi:10.3390/en12061059

Danqi Li, Fei Mei + Show 3 more

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https://doi.org/10.3390/en12061059

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Abstract

A self-supervised voltage sag source identification method based on a convolution neural network is proposed in this study. In addition, a self-supervised CNN (Convolutional Neural Networks) voltage sag source identification model is constructed on the basis of the convolution neural network and AutoEncoder. The convolution layer and pool layer in CNN are used to extract the voltage sag characteristics, and the self-supervised network training process is realized based on the principle of AE. In the constructed mode, features which reflect the data characteristics are used rather than artificial features, thus improving the accuracy of practical application. It is unnecessary to input a lot of correct labels before the self-supervised training process. The model can meet the requirements of sag source identification on timeliness, practicability, diversity, and versatility in the context of modern big data. In this study, three-phase asymmetric sag sources in sag sources are classified into more detailed categories according to different fault phases. Therefore, the proposed method can not only identify the voltage sag source, but also accurately determine the specific fault phase. Finally, the optimal parameters of the model are recognized through a case study, and a self-supervised CNN model is established based on the data type of voltage sag. This model extracts features and identifies sag sources through the measured sag data. The superiority of the proposed method is verified by a comparison.

Highlights

With the development of industrial equipment as well as electrical automation and intellectualization of buildings, influences of voltage sag on the production and operation of large industrial and commercial users is becoming more and more prominent [1,2,3,4], especially in fields with extensive applications of power electronic devices and sensitive to voltage sags, such as semiconductor manufacturing, precise instrument processing, automotive manufacturing, and other industries
The experimental results showed that the accuracy of the sag source identification method using the SVM method is 83% and the accuracy of the method based on Convolutional Neural Networks (CNN) is 97%
A self-supervised voltage sag source identification method based on CNN is proposed

Summary

Introduction

With the development of industrial equipment as well as electrical automation and intellectualization of buildings, influences of voltage sag on the production and operation of large industrial and commercial users is becoming more and more prominent [1,2,3,4], especially in fields with extensive applications of power electronic devices and sensitive to voltage sags, such as semiconductor manufacturing, precise instrument processing, automotive manufacturing, and other industries. As a common power quality problem, voltage sag can be caused by many factors, such as motor start, transformer switching, short circuit fault, etc. The production interruption and delay caused by voltage sag disturbance shows an obvious upward trend [7]. The direct and indirect economic losses caused by voltage sag disturbance are becoming more and more serious, which puts forward higher requirements for the quality of power supply. Accurate identification of sag sources and their fault phases is conducive to analyze, compensate and suppress local voltage sag. It can be Energies 2019, 12, 1059; doi:10.3390/en12061059 www.mdpi.com/journal/energies

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