Generative Adversarial Model-Guided Deep Active Learning for Voltage Dip Labelling

Document identifier: oai:DiVA.org:ltu-76910
Access full text here:10.1109/PTC.2019.8810499
Keyword: Engineering and Technology, Electrical Engineering, Electronic Engineering, Information Engineering, Other Electrical Engineering, Electronic Engineering, Information Engineering, Teknik och teknologier, Elektroteknik och elektronik, Annan elektroteknik och elektronik, Automatic Labelling, Deep Active Learning, Deep Learning, Generative-Discriminative Model, Semi-supervised Training, Voltage Dip, Electric Power Engineering, Elkraftteknik
Publication year: 2019
Relevant Sustainable Development Goals (SDGs):
SDG 9 Industry, innovation and infrastructure
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

In many real applications, the ground truths of class labels from voltage dip sequences used for training a voltage dip classification system are unknown, and require manual labelling by human experts. This paper proposes a novel deep active learning method for automatic labelling of voltage dip sequences used for the training process. We propose a novel deep active learning method, guided by a generative adversarial network (GAN), where the generator is formed by modelling data with a Gaussian mixture model and provides the estimated probability distribution function (pdf) where the query criterion of the deep active learning method is built upon. Furthermore, the discriminator is formed by a support vector machine (SVM). The proposed method has been tested on a voltage dip dataset (containing 916 dips) measured in a European country. The experiments have resulted in good performance (classification rate 83% and false alarm 3.2%), which have demonstrated the effectiveness of the proposed method.

Authors

Azam Bagheri

Luleå tekniska universitet; Energivetenskap
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Irene Y.H. Gu

Department of Electrical Engineering Chalmers University of Technology Gothenburg, Sweden
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Math Bollen

Luleå tekniska universitet; Energivetenskap
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