Basulaiman, Kamal Aboud
(2024)
Learning Fast Approximations For Nonconvex Optimization Problems Via Deep Learning With Applications To Power Systems.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Nonlinear convex optimization has provided a great modeling language and a powerful solution tool for the control and analysis of power systems over the last decade. A main challenge today is solving non-convex problems in real-time. However, if an oracle can guess, ahead of time, a high quality initial solution, then most non-convex optimization problems can be solved in a limited number of iterations using off-the-shelf solvers. In this proposal, we study how deep learning can provide good approximations for real-time power system applications. These approximations can act as good initial solutions to any exact algorithm. Alternatively, such approximations could be satisfactory to carry out real-time operations in power systems.
First, we address the problem of joint power system state estimation and bad data identification. We propose a deep learning model that provides high quality approximations in milliseconds.
Second, we address the problem multi-step ahead power system state forecasting and advocate sequence-to-sequence models for better representation.
Lastly, we study the problem of learning fast approximations of the optimal basis of a linear program produced by the simplex algorithm. We cast the problem as a simple classification task and propose a deep learning model.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
11 January 2024 |
| Date Type: |
Publication |
| Defense Date: |
13 November 2023 |
| Approval Date: |
11 January 2024 |
| Submission Date: |
10 November 2023 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
96 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Industrial Engineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Graph Convolution Neural Network, Power State Estimation, Warm-start, Non-convex Optimization, Mixed Integer Programming. |
| Date Deposited: |
11 Jan 2024 19:25 |
| Last Modified: |
11 Jan 2024 19:25 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/45510 |
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