A fast identification algorithm for Linear Parameter Varying Vector AR models of short-term drivetrain vibration

Luis David Avendaño-Valencia; Andriana Georgantopoulou

doi:10.7712/150123.9865.451289

A fast identification algorithm for Linear Parameter Varying Vector AR models of short-term drivetrain vibration

Luis David Avendaño-Valencia^*, Andriana Georgantopoulou^*

^*Corresponding author for this work

Aarhus University

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

Wind turbine drivetrain vibrations are characterised by very complex dynamics originating from the convolution of deterministic and stochastic excitation sources moving through the structural dynamics of the wind turbine. In this work we postulate Linear Parameter Varying Vector AutoRegressive (LPV-VAR) models to represent those signals. To deal with the complexity of these models during the identification procedure, we devise an algorithm, based on the QR decomposition of the regression matrix, to accelerate the model identification procedure. The proposed methods are demonstrated on data from a wind turbine drivetrain simulator.

Original language	English
Title of host publication	Eccomas Proceedia SMART
Publisher	ECCOMAS Proceedia
Publication date	2023
Pages	1161-1172
ISBN (Electronic)	978-960-88104-6-4
DOIs	https://doi.org/10.7712/150123.9865.451289
Publication status	Published - 2023
Event	10th ECCOMAS Thematic Conference on Smart structure and Materials - Patras, Greece Duration: 3. Jul 2023 → 5. Jul 2023

Conference

Conference	10th ECCOMAS Thematic Conference on Smart structure and Materials
Country/Territory	Greece
City	Patras
Period	03/07/2023 → 05/07/2023

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10.7712/150123.9865.451289

Cite this

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title = "A fast identification algorithm for Linear Parameter Varying Vector AR models of short-term drivetrain vibration",

abstract = "Wind turbine drivetrain vibrations are characterised by very complex dynamics originating from the convolution of deterministic and stochastic excitation sources moving through the structural dynamics of the wind turbine. In this work we postulate Linear Parameter Varying Vector AutoRegressive (LPV-VAR) models to represent those signals. To deal with the complexity of these models during the identification procedure, we devise an algorithm, based on the QR decomposition of the regression matrix, to accelerate the model identification procedure. The proposed methods are demonstrated on data from a wind turbine drivetrain simulator.",

author = "Avenda{\~n}o-Valencia, {Luis David} and Andriana Georgantopoulou",

year = "2023",

doi = "10.7712/150123.9865.451289",

language = "English",

pages = "1161--1172",

booktitle = "Eccomas Proceedia SMART",

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T1 - A fast identification algorithm for Linear Parameter Varying Vector AR models of short-term drivetrain vibration

AU - Avendaño-Valencia, Luis David

AU - Georgantopoulou, Andriana

PY - 2023

Y1 - 2023

N2 - Wind turbine drivetrain vibrations are characterised by very complex dynamics originating from the convolution of deterministic and stochastic excitation sources moving through the structural dynamics of the wind turbine. In this work we postulate Linear Parameter Varying Vector AutoRegressive (LPV-VAR) models to represent those signals. To deal with the complexity of these models during the identification procedure, we devise an algorithm, based on the QR decomposition of the regression matrix, to accelerate the model identification procedure. The proposed methods are demonstrated on data from a wind turbine drivetrain simulator.

AB - Wind turbine drivetrain vibrations are characterised by very complex dynamics originating from the convolution of deterministic and stochastic excitation sources moving through the structural dynamics of the wind turbine. In this work we postulate Linear Parameter Varying Vector AutoRegressive (LPV-VAR) models to represent those signals. To deal with the complexity of these models during the identification procedure, we devise an algorithm, based on the QR decomposition of the regression matrix, to accelerate the model identification procedure. The proposed methods are demonstrated on data from a wind turbine drivetrain simulator.

U2 - 10.7712/150123.9865.451289

DO - 10.7712/150123.9865.451289

M3 - Article in proceedings

SP - 1161

EP - 1172

BT - Eccomas Proceedia SMART

PB - ECCOMAS Proceedia

T2 - 10th ECCOMAS Thematic Conference on Smart structure and Materials

Y2 - 3 July 2023 through 5 July 2023

ER -

A fast identification algorithm for Linear Parameter Varying Vector AR models of short-term drivetrain vibration

Abstract

Conference

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