Date of Award
2020
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Safwan Wshah
Abstract
In power systems, accurate device modeling is crucial for grid reliability, availability, and resiliency. Many critical tasks such as planning or even realtime operation decisions rely on accurate modeling. This research presents an approach for model parameter calibration in power system models using deep learning. Existing calibration methods are based on mathematical approaches that suffer from being ill-posed and thus may have multiple solutions. We are trying to solve this problem by applying a deep learning architecture that is trained to estimate model parameters from simulated Phasor Measurement Unit (PMU) data. The data recorded after system disturbances proved to have valuable information to verify power system devices. A quantitative evaluation of the system results is provided. Results showed high accuracy in estimating model parameters of 0.017 MSE on the testing dataset. We also provide that the proposed system has scalability under the same topology. We consider these promising results to be the basis for further exploration and development of additional tools for parameter calibration.
Language
en
Number of Pages
58 p.
Recommended Citation
Wu, Yuhao, "Model Parameter Calibration in Power Systems" (2020). Graduate College Dissertations and Theses. 1248.
https://scholarworks.uvm.edu/graddis/1248