Learning Spatiotemporal Correlations for Missing Noisy PMU Data Correction in Smart Grid

While various promising phasor measurement unit (PMU) data-driven applications have been developed for modern smart grids, how to improve the overall PMU data quality to ensure the reliability of these applications in practice still remains an open issue. Considering the challenging task of missing PMU data correction (MPDC) in practical complicated and noisy measurement contexts, this article develops a novel spatiotemporal correlation learning scheme (SCLS) for online MPDC in smart grids. In particular, the SCLS is strategically realized with two successive modules. First, from four complementary spatiotemporal perspectives, statistical missing data imputation is carried out to derive initial correction results in noisy contexts. Second, a well-designed deep learning architecture with the integration of convolutional neural network (CNN) and residual learning techniques is introduced to refine the correction results. With the help of these two modules, the SCLS is capable of performing precise MPDC for regional PMU measurements as well as filtering out potential noises. Extensive numerical test results on the IEEE 39-bus test system and the real-world Guangdong power grid in South China demonstrate the efficacy of the SCLS in practical complicated contexts.

Automated summary

This article introduces a novel spatiotemporal correlation learning scheme (SCLS) for online missing PMU data correction (MPDC) in challenging measurement contexts, showing high efficacy in refining correction results and filtering out potential noises.