Research on Generation Technology of Small Sample Data Based on Generative Adversarial Network

doi:10.3969/j.issn.1000-7229.2019.05.009

Abstract

Abstract: Data-driven deep learning technology has become the application trend of the new generation of smart grids， which puts higher demands on the magnitude of the labeled training data in the grid. In order to obtain more labeled smart grid sample data， this paper proposes a training sample generation algorithm based on improved generative adversarial network. The method improves the generation model and discriminant model of GAN through alternate training， and does not need the guidance of prior knowledge to autonomously learn the distribution law of the original samples and generate new data samples. Then the artificial neural network is used as the basic classifier to calculate the accuracy of the sample classification and verify the validity of the generated samples. Experiments have shown that the improved GAN model can effectively learn the distribution law of samples and improve the accuracy of harmonic classification. This method has good anti-noise ability and generalization， which is of great significance for development of deep learning technology in smart grid.

Key words: smart grid, deep learning, full convolutional network, generative adversarial network, sample generation

CLC Number:

TM 714

YANG Yinan， QI Linhai， WANG Hong， SU Linping. Research on Generation Technology of Small Sample Data Based on Generative Adversarial Network[J]. Electric Power Construction, 2019, 40(5): 71-77.

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