DOI:10.35833/MPCE.2021.000783 Synthetic PMU Data Creation Based on Generative Adversarial Network Under Time-varying Load Conditions Page view: 32        Net amount: 89 Author: Xiangtian Zheng1, Andrea Pinceti2, Lalitha Sankar3, Le Xie1 Author Affiliation: 1.Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77840, USA 2.Dominion Energy, Richmond, VA 23219, USA 3.School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85281, USA Foundation: The work was supported by the National Science Foundation (No. OAC-1934675, No. ECCS-2035688, No. ECCS-1611301). Abstract: In this study, a machine learning based method is proposed for creating synthetic eventful phasor measurement unit (PMU) data under time-varying load conditions. The proposed method leverages generative adversarial networks to create quasi-steady states for the power system under slowly-varying load conditions and incorporates a framework of neural ordinary differential equations (ODEs) to capture the transient behaviors of the system during voltage oscillation events. A numerical example of a large power grid suggests that this method can create realistic synthetic eventful PMU voltage measurements based on the associated real PMU data without any knowledge of the underlying nonlinear dynamic equations. The results demonstrate that the synthetic voltage measurements have the key characteristics of real system behavior on distinct time scales. Keywords: Synthetic phasor measurement unit data ; generative adversarial networks ; neural ordinary differential equations ; data-driven method Received:December 03, 2021               Online Time:2023/01/28 View Full Text       Download reader