Structure-Informed Graph Learning of Networked Dependencies for Online Prediction of Power System Transient Dynamics

Zhao, Tianqiao; Yue, Meng; Wang, Jianhui

doi:10.1109/tpwrs.2022.3153328

Title: Structure-Informed Graph Learning of Networked Dependencies for Online Prediction of Power System Transient Dynamics

Journal Article · 22 February 2022 · IEEE Transactions on Power Systems

DOI: https://doi.org/10.1109/tpwrs.2022.3153328 · OSTI ID:1992865

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Brookhaven National Laboratory (BNL), Upton, NY (United States)
Southern Methodist Univ., Dallas, TX (United States)

Online transient analysis plays an increasingly important role in dynamic power grids as the renewable generation continues growing. Traditional numerical methods for transient analysis not only are computationally intensive but also require precise contingency information as input, and therefore, are not suitable for online applications. Existing online transient assessment studies focus on the determination of post-contingency system stability or stability margin. Here, this paper develops a novel graph-learning framework, Deep-learning Neural Representation or DNR, for online prediction, of the time-series trajectories of the system states using initial system responses that can be measured by phasor measurement units (PMUs). The proposed DNR framework consists of two sequential modules: a Network Constructor that captures network dependencies among generators, and a Dynamics Predictor that predicts the system trajectories. The key to improved prediction performance is the introduction of the spatio-temporal message-passing operations into graph neural networks with structural knowledge. Its effectiveness and scalability are validated through comparative studies, demonstrating the prediction performance under different contingency scenarios for systems of different sizes. This framework provides a solution to online predicting post-fault system dynamics based on real-time PMU measurements. Additionally, it can also be applied to facilitate the offline transient simulation without simulating the entire trajectories.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Electricity (OE), Advanced Grid Research & Development. Power Systems Engineering Research

Grant/Contract Number:: SC0012704

OSTI ID:: 1992865

Report Number(s):: BNL-224605-2023-JAAM

Journal Information:: IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 6 Vol. 37; ISSN 0885-8950

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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24 POWER TRANSMISSION AND DISTRIBUTION
dynamics reproduction
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machine learning
spatial-temporal dependencies
transient stability

Title: Structure-Informed Graph Learning of Networked Dependencies for Online Prediction of Power System Transient Dynamics

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