WIND TURBINE ROTOR ACCELERATION: IDENTIFICATION USING GAUSSIAN REGRESSION
Author(s): W. E. Leithead, Yunong Zhang, Kian Seng Neo
Type: Conference Paper (Proc. of the 2nd ICINCO)
Date: September 2005
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. 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.
Conferences
- Leithead, W. E., Neo, Kian Seng and Leith, D. J., Gaussian regression based on models with two stochastic processes, 16th IFAC World Congress, Prague, July 2005.
- Leithead, W. E., Zhang, Yunong and Neo, Kian Seng, Wind turbine rotor acceleration: identification using Gaussian regression, 2nd ICINCO, Barcelona, September 2005.
- Neo, Kian Seng, Leithead, W. E., Zhang, Yunong, Multi-frequency scale Gaussian regression for noisy time-series data, 6th bi-ennial UKACC Control Conference, International Control Conference 2006, Glasgow, September 2006. (Accepted)
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