Sample-Efficient Learning for a Surrogate Model of Three-Phase Distribution System
SCIE
SCOPUS
- Title
- Sample-Efficient Learning for a Surrogate Model of Three-Phase Distribution System
- Authors
- Nguyen, Hoang Tien; Kim, Young-Jin; Choi, Dae-Hyun
- Date Issued
- 2024-01
- Publisher
- Institute of Electrical and Electronics Engineers
- Abstract
- A surrogate model that accurately predicts distribution system voltages is crucial for reliable smart grid planning and operation. This letter proposes a fixed-point data-driven surrogate modeling method that employs a limited dataset to learn the power-voltage relationship of an unbalanced three-phase distribution system. The proposed surrogate model is designed using a fixed-point load-flow equation, and the stochastic gradient descent method with an automatic differentiation technique is employed to update the parameters of the surrogate model using complex power and voltage samples. Numerical examples in IEEE 13-bus, 37-bus, and 123-bus systems demonstrate that the proposed surrogate model can outperform surrogate models based on the deep neural network and Gaussian process regarding prediction accuracy and sample efficiency.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/120308
- DOI
- 10.1109/tpwrs.2023.3334080
- ISSN
- 0885-8950
- Article Type
- Article
- Citation
- IEEE Transactions on Power Systems, vol. 39, no. 1, page. 2361 - 2364, 2024-01
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.