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\begin{document}

\title{An Online Updated Linear Power Flow Model Based on Regression Learning}



\author[1]{Molin An}%
\author[1]{Tianguang Lu}%
\author[1]{Xueshan Han}%
\affil[1]{Shandong University}%


\vspace{-1em}



  
  \date{June 13, 2024}


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\begin{abstract}
An online updated data-driven linear power flow (LPF) model based on
regression learning is proposed in this paper. We obtain a quadratic
power flow model through regression learning first, and then derive the
normal and incremental forms of LPF models by Taylor expansion. The
parameters of LPF model are updated online, which improves the
generalization ability. After only one initial regression learning, the
proposed data-driven LPF model avoids model retraining when updated. The
new parameter of the proposed model is simply calculated according to
the real-time measurement data. Therefore, the LPF model we proposed is
accurate, generalizable, and greatly minimizes the data consumption and
running time. Performance analysis verifies the superiority of the
proposed method.%
\end{abstract}%



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\textbf{Hosted file}

\verb`An Online Updated Linear Power Flow Model Based on Regression Learning.docx`  available at \url{https://authorea.com/users/761473/articles/737547-an-online-updated-linear-power-flow-model-based-on-regression-learning}

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