A Novel Method for Missing Data Reconstruction in Smart Grid Using Generative Adversarial Networks
Jiashu Fang, Le Zheng, Chongru Liu
Abstract
Existing machine-learning research on power grids relies on online measurements without missing data. We propose a missing data reconstruction model based on generative adversarial networks to supplement existing methods. This model fits better spatio-temporal data with several improvements over previous approaches. First, the loss function considers both distribution and value differences, leveraging all available information to minimize differences between original and generated data. Then, a deep-learning architecture incorporating convolutional neural layers and nonlocal blocks is developed to extract the spatial-temporal information in electrical feature maps. The proposed method exhibits enhanced credibility by neglecting invalid consecutive data under phasor measurement unit (PMU) failures (proven by attention maps generated in nonlocal blocks), and higher accuracy than existing models for recovering data under random data missing/PMU failure conditions (proven by numerical results). Finally, the proposed data reconstruction model is effectively applied to an online framework for transient stability assessment.