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

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.

Automated summary

Data-driven linear power flow (DL-PF) models are widely used due to their accuracy, but training data may be insufficient in practice, leading to unreasonable results. To address this issue, this letter proposes a physical-model-aided data-driven linear power flow (PD-LPF) model that introduces physical model parameters to assist the training process, ensuring linearization accuracy for critical operating points. The PD-LPF model is applicable for both transmission and distribution systems, and exhibits excellent accuracy and robustness under severe missing-data conditions when compared to current LPF models.