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\begin{document}
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\title{Shapelet-based Method for Short-Term Voltage Stability Assessment
considering Interaction Mechanisms in Multi-Infeed HVDC System}



\author[1]{Yubo Sun}%
\author[1]{Shuqing Zhang}%
\author[1]{Chao Lu}%
\affil[1]{Tsinghua University}%


\vspace{-1em}



  
  \date{April 25, 2026}


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\begin{abstract}
The short-term voltage stability (STVS) of a multi-infeed high-voltage
direct current (MIHVDC) system is controlled by complex dynamic reactive
power interactions that have multifactor coupling and strong
time-varying characteristics. However, because of the complexity in
decoupling these interactions, traditional analytical methods cannot
accurately quantify their effects on stability. The STVS analysis of
MIHVDC systems involves several challenges, such as high-dimensional,
time-varying, and nonlinear characteristics. To address these
challenges, this paper proposes an STVS evaluation method based on a
multi-time-scale (MTS) interaction mechanism and time-series data
mining. First, a multi-timescale interaction mechanism is proposed on
the basis of dynamic responses. The short-term voltage instability
process can be divided into three stages, namely, power flow transfer,
dynamic reactive power regulation, and hierarchical cascade
electromechanical interactions. This clarifies the accumulation and
propagation mechanisms of reactive power deficiency at different
timescales. Second, this paper proposes a time-series data mining method
based on shapelets to solve problems in traditional STVS analysis.
However, the dynamic response caused by commutation failure of the HVDC
system interferes with the accuracy of time-series data mining. To solve
this problem, this paper presents the optimization of the method by
combining it with the dynamic mechanism and proposes the synchronous
cluster of shapelets (SynCShapelet) method. In addition, the physical
relationship between the shapelet and critical operating point of the
dynamic load is elucidated, thus addressing the black-box problem and
low confidence of machine learning methods. As demonstrated via a case
study, SynCShapelet can predict the instability of the system by
detecting features and incorporating the MTS interaction mechanism to
preliminarily assess the path of the short-term voltage instability. In
the application scenario of the STVS of MIHVDC, the proposed method
provides a theoretical basis and technical support for the STVS
evaluation and control strategy.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Shapelet-based Method for Short-Term Voltage Stability Assessment considering Interaction Mechanisms in Multi-Infeed HVDC System_Submission.docx`  available at \url{https://authorea.com/users/950734/articles/1320262-shapelet-based-method-for-short-term-voltage-stability-assessment-considering-interaction-mechanisms-in-multi-infeed-hvdc-system}

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