DeepONet Based Uncertainty Quantification for Power System Dynamics with Stochastic Loads-Reference-Cited by-同舟云学术

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DeepONet Based Uncertainty Quantification for Power System Dynamics with Stochastic Loads

Published:2023-07-16 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Power & Energy Society General Meeting (PESGM)
language:
Short-container-title:

Author:

Ye Ketian¹,Zhao Junbo¹,Liu Xiaodong²,Moya Christian³,Lin Guang³

Affiliation:

1. University of Connecticut,Department of Electrical and Computer Engineering,Storrs,CT,USA,06269

2. Eversource Energy,Berlin,CT,USA,06037

3. Purdue University,Department of Mathematics and School of Mechanical Engineering,West Lafayette,IN,USA,47906

Funder

National Science Foundation

Brookhaven National Laboratory

Advanced Scientific Computing Research

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10252165/10252088/10252937.pdf?arnumber=10252937

Reference18 articles.

1. Quasi-Monte Carlo Based Probabilistic Small Signal Stability Analysis for Power Systems With Plug-In Electric Vehicle and Wind Power Integration

2. Importance Sampling of Injected Powers for Electric Power System Security Analysis

3. Stochastic Power System Simulation Using the Adomian Decomposition Method;duan;arXiv preprint arXiv 1710 02415,2021

4. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

5. A stochastic model for power system transient stability with wind power

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