Sample-Efficient Learning for a Surrogate Model of Three-Phase Distribution System
- Authors
- Nguyen, Hoang Tien; Kim, Young-Jin; Choi, Dae-Hyun
- Issue Date
- Jan-2024
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Computational modeling; Load modeling; Machine learning; Mathematical models; Predictive models; stochastic gradient descent; surrogate model; Testing; three-phase distribution system; Training; Voltage control
- Citation
- IEEE Transactions on Power Systems, v.39, no.1, pp 2361 - 2364
- Pages
- 4
- Journal Title
- IEEE Transactions on Power Systems
- Volume
- 39
- Number
- 1
- Start Page
- 2361
- End Page
- 2364
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/70667
- DOI
- 10.1109/TPWRS.2023.3334080
- ISSN
- 0885-8950
1558-0679
- 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. IEEE
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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