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Physical-Model-Aided Data-Driven Linear Power Flow Model: An Approach to Address Missing Training Data
IEEE Transactions on Power Systems ( IF 6.6 ) Pub Date : 2023-03-13 , DOI: 10.1109/tpwrs.2023.3256120
Zhentong Shao, Qiaozhu Zhai, Xiaohong Guan

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.

中文翻译:

物理模型辅助数据驱动的线性功率流模型:一种解决训练数据缺失的方法

数据驱动的线性功率流 (D-LPF) 模型因其出色的准确性而广受欢迎。通常,D-LPF 模型依赖于足够的训练数据。然而,在实践中,由于记录错误或测量条件有限,训练数据可能不足。为了解决这个实际和重要的问题,这封信提出了一种物理模型辅助的数据驱动线性功率流(PD-LPF)模型,其中引入物理模型参数来辅助数据驱动的训练过程,从而避免不合理的训练结果,并以最大概率保证关键操作点的线性化精度。所提出的方法适用于输电和配电系统。与当前的 LPF 模型相比,PD-LPF 模型在严重缺失数据的情况下表现出出色的准确性和鲁棒性。
更新日期:2023-03-13
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