A review on applications of holomorphic embedding methods

The holomorphic embedding method (HEM) stands as a mathematical technique renowned for its favorable convergence properties when resolving algebraic systems involving complex variables. The key idea behind the HEM is to convert the task of solving complex algebraic equations into a series expansion...

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Main Authors: Kaiyang Huang, Kai Sun
Format: Article
Language:English
Published: Tsinghua University Press 2023-12-01
Series:iEnergy
Subjects:
Online Access:https://www.sciopen.com/article/10.23919/IEN.2023.0037
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author Kaiyang Huang
Kai Sun
author_facet Kaiyang Huang
Kai Sun
author_sort Kaiyang Huang
collection DOAJ
description The holomorphic embedding method (HEM) stands as a mathematical technique renowned for its favorable convergence properties when resolving algebraic systems involving complex variables. The key idea behind the HEM is to convert the task of solving complex algebraic equations into a series expansion involving one or multiple embedded complex variables. This transformation empowers the utilization of complex analysis tools to tackle the original problem effectively. Since the 2010s, the HEM has been applied to steady-state and dynamic problems in power systems and has shown superior convergence and robustness compared to traditional numerical methods. This paper provides a comprehensive review on the diverse applications of the HEM and its variants reported by the literature in the past decade. The paper discusses both the strengths and limitations of these HEMs and provides guidelines for practical applications. It also outlines the challenges and potential directions for future research in this field.
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spelling doaj.art-bd603a8a62d3408889a2eac68d039bb72024-01-15T14:27:52ZengTsinghua University PressiEnergy2771-91972023-12-012426427410.23919/IEN.2023.0037A review on applications of holomorphic embedding methodsKaiyang Huang0Kai Sun1Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USADepartment of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USAThe holomorphic embedding method (HEM) stands as a mathematical technique renowned for its favorable convergence properties when resolving algebraic systems involving complex variables. The key idea behind the HEM is to convert the task of solving complex algebraic equations into a series expansion involving one or multiple embedded complex variables. This transformation empowers the utilization of complex analysis tools to tackle the original problem effectively. Since the 2010s, the HEM has been applied to steady-state and dynamic problems in power systems and has shown superior convergence and robustness compared to traditional numerical methods. This paper provides a comprehensive review on the diverse applications of the HEM and its variants reported by the literature in the past decade. The paper discusses both the strengths and limitations of these HEMs and provides guidelines for practical applications. It also outlines the challenges and potential directions for future research in this field.https://www.sciopen.com/article/10.23919/IEN.2023.0037holomorphic embedding methodpower flowpolynomial solutionsnonlinear algebraic equationsdifferential equations
spellingShingle Kaiyang Huang
Kai Sun
A review on applications of holomorphic embedding methods
iEnergy
holomorphic embedding method
power flow
polynomial solutions
nonlinear algebraic equations
differential equations
title A review on applications of holomorphic embedding methods
title_full A review on applications of holomorphic embedding methods
title_fullStr A review on applications of holomorphic embedding methods
title_full_unstemmed A review on applications of holomorphic embedding methods
title_short A review on applications of holomorphic embedding methods
title_sort review on applications of holomorphic embedding methods
topic holomorphic embedding method
power flow
polynomial solutions
nonlinear algebraic equations
differential equations
url https://www.sciopen.com/article/10.23919/IEN.2023.0037
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