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Recognition of Voltage Sag Sources Based on Phase Space Reconstruction and Improved VGG Transfer Learning

1
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
2
China Mobile (Hangzhou) Information Technology Co., Ltd., Hangzhou 311100, China
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Author to whom correspondence should be addressed.
Entropy 2019, 21(10), 999; https://doi.org/10.3390/e21100999
Received: 4 September 2019 / Revised: 30 September 2019 / Accepted: 30 September 2019 / Published: 12 October 2019
The recognition of the voltage sag sources is the basis for formulating a voltage sag governance plan and clarifying the responsibility for the accident. Aiming at the recognition problem of voltage sag sources, a recognition method of voltage sag sources based on phase space reconstruction and improved Visual Geometry Group (VGG) transfer learning is proposed from the perspective of image classification. Firstly, phase space reconstruction technology is used to transform voltage sag signals, generate reconstruction images of voltage sag, and analyze the intuitive characteristics of different sag sources from reconstruction images. Secondly, combined with the attention mechanism, the standard VGG 16 model is improved to extract the features completely and prevent over-fitting. Finally, VGG transfer learning model uses the idea of transfer learning for training, which improves the efficiency of model training and the recognition accuracy of sag sources. The purpose of the training model is to minimize the cross entropy loss function. The simulation analysis verifies the effectiveness and superiority of the proposed method. View Full-Text
Keywords: phase space reconstruction; VGG; transfer learning; voltage sag source; attention; cross entropy phase space reconstruction; VGG; transfer learning; voltage sag source; attention; cross entropy
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Pu, Y.; Yang, H.; Ma, X.; Sun, X. Recognition of Voltage Sag Sources Based on Phase Space Reconstruction and Improved VGG Transfer Learning. Entropy 2019, 21, 999.

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