Self-Supervised Voltage Sag Source Identification Method Based on CNN

Danqi Li, Fei Mei, Chenyu Zhang, Haoyuan Sha and Jianyong Zheng
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Danqi Li: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Fei Mei: Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China
Chenyu Zhang: State Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 211113, China
Haoyuan Sha: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Jianyong Zheng: School of Electrical Engineering, Southeast University, Nanjing 210096, China

Energies, 2019, vol. 12, issue 6, 1-14

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.

Keywords: voltage sag; convolutional neural network; AutoEncoder; grayscale; sag source identification (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: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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