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Recursive versus nonrecursive Richardson algorithms: systematic overview, unified frameworks and application to electric grid power quality monitoring

Automatika, 2022-04, Vol.63 (2), p.328-337 [Peer Reviewed Journal]

2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2022 ;2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0005-1144 ;EISSN: 1848-3380 ;DOI: 10.1080/00051144.2022.2039989

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  • Title:
    Recursive versus nonrecursive Richardson algorithms: systematic overview, unified frameworks and application to electric grid power quality monitoring
  • Author: Stotsky, Alexander
  • Subjects: Algorithms ; Automation & Control Systems ; Computational efficiency ; Computing time ; Convergence ; Decomposition ; Electric power grids ; Electric power systems ; Engineering ; frequency ; inverses ; iterative method ; Iterative methods ; Linear equations ; matrix ; Monitoring ; Newton-Schulz matrix inversion algorithms ; Optimization ; Parameter estimation ; power ; power quality monitoring ; quality monitoring ; Richardson algorithms ; Robotics ; Robotteknik och automation
  • Is Part Of: Automatika, 2022-04, Vol.63 (2), p.328-337
  • Description: Sufficiently accurate, fast and computationally efficient solution of the system of linear equations is required in many estimation problems. Richardson iteration is one of the main solvers for linear equations, which provides optimization possibilities for time critical and accuracy critical applications. Convergence rate improvement and reduction of the computational complexity of the Richardson iteration are the most important problems in the area. The introduction of Newton-Schulz iterations is the efficient way for convergence rate improvement and the paper starts with systematic overview of the high-order Newton-Schulz matrix inversion algorithms. In addition, the unified framework for recursive computationally efficient convergence accelerators and error models for a number of combinations of Richardson and Newton-Schulz iterations is developed. A new nonrecursive parameter estimation concept is introduced and compared in this paper with recursive estimation. Recursive and nonrecursive Richardson algorithms together with the standard LU decomposition method were applied to the electric grid power quality monitoring problem. The algorithms were tested for the detection of the sag and swell signatures in the voltage and current signals on real data in three-phase power system. Nonrecursive Richardson algorithms which save close to half of the computational time compared to LU decomposition method were recommended for power quality monitoring applications.
  • Publisher: Ljubljana: Taylor & Francis
  • Language: English;Croatian
  • Identifier: ISSN: 0005-1144
    EISSN: 1848-3380
    DOI: 10.1080/00051144.2022.2039989
  • Source: Taylor & Francis OA刊
    Alma/SFX Local Collection
    SWEPUB Freely available online
    ProQuest Central
    DOAJ Directory of Open Access Journals
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