Physical-Model-Aided Data-Driven Linear Power Flow Model: An Approach to Address Missing Training Data

doi:10.1109/TPWRS.2023.3256120

Physical-Model-Aided Data-Driven Linear Power Flow Model: An Approach to Address Missing Training Data

2023-05-01

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DOI：10.1109/TPWRS.2023.3256120

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Data-driven linear power flow (D-LPF) models are prevalent due to their excellent accuracy. Typically, D-LPF models rely on sufficient training data. However, in practice, the training data may be insufficient due to recording errors or limited measurement conditions. To address this practical and important issue, this letter presents a physical-model-aided data-driven linear power flow (PD-LPF) model, in which, physical model parameters are introduced to assist the data-driven training process, thereby avoiding unreasonable training results, and guaranteeing linearization accuracy for critical operating points with the maximum probability. The proposed method is applicable for both transmission and distribution systems. Compared to current LPF models, the PD-LPF model exhibits excellent accuracy and robustness under severe missing-data conditions.

Keywords:

Data models

Modeling

Training

Training data

Mathematical models

Load flow

Computational modeling

Data-driven

missing data

Index Terms

linear power flow

chance constraints

distributionally robust

Physical-Model-Aided Data-Driven Linear Power Flow Model: An Approach to Address Missing Training Data

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