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Authors: W. E. Leithead 1 ; Yunong Zhang 2 and Kian Seng Neo 2

Affiliations: 1 University of Strathclyde; Hamilton Institute, National University of Ireland, Ireland ; 2 Hamilton Institute, National University of Ireland, Ireland

Keyword(s): Data analysis, Gaussian regression, independent processes, fast algorithms.

Related Ontology Subjects/Areas/Topics: Informatics in Control, Automation and Robotics ; Signal Processing, Sensors, Systems Modeling and Control ; System Identification

Abstract: Gaussian processes prior model methods for data analysis are applied to wind turbine time series data to identify both rotor speed and rotor acceleration from a poor measurement of rotor speed. In so doing, two issues are addressed. Firstly, the rotor speed is extracted from a combined rotor speed and generator speed measurement. A novel adaptation of Gaussian process regression based on two independent processes rather than a single process is presented. Secondly, efficient algorithms for the manipulation of large matrices are required. The Toeplitz nature of the matrices is exploited to derive novel fast algorithms for the Gaussian process methodology that are memory efficient.

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Paper citation in several formats:
E. Leithead, W.; Zhang, Y. and Seng Neo, K. (2005). WIND TURBINE ROTOR ACCELERATION: IDENTIFICATION USING GAUSSIAN REGRESSION. In Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 972-8865-31-7; ISSN 2184-2809, SciTePress, pages 84-91. DOI: 10.5220/0001179300840091

@conference{icinco05,
author={W. {E. Leithead}. and Yunong Zhang. and Kian {Seng Neo}.},
title={WIND TURBINE ROTOR ACCELERATION: IDENTIFICATION USING GAUSSIAN REGRESSION},
booktitle={Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2005},
pages={84-91},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001179300840091},
isbn={972-8865-31-7},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - WIND TURBINE ROTOR ACCELERATION: IDENTIFICATION USING GAUSSIAN REGRESSION
SN - 972-8865-31-7
IS - 2184-2809
AU - E. Leithead, W.
AU - Zhang, Y.
AU - Seng Neo, K.
PY - 2005
SP - 84
EP - 91
DO - 10.5220/0001179300840091
PB - SciTePress