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A Potential Method for Determining Nonlinearity in Wind Data
MIN GAN1; HAN-XIONG LI2,3; C. L. PHILIP CHEN4; LONG CHEN4
2015
Conference NameIEEE Transactions on Power Systems
Source PublicationIEEE Power and Energy Technology Systems Journal
Volume2
Issue2
Pages74-81
Conference Date2015
Conference PlaceUSA
Abstract

This paper investigates a basic problem in modeling time series of wind data: whether there exists detectable correlation or nonlinearity in the observed wind time series. At present, a variety of linear and nonlinear time series models have been applied to predict the wind data. The first question that should be answered before building a model, however, is whether the data studied are correlated or carry nonlinearity. It would be futile to model the relationships if the pertaining wind data cannot be distinguished from the white noise. Advanced nonlinear prediction models are also not necessary if there are no nonlinear structures in the data. In this paper, we test by the surrogate data method: 1) whether the differenced wind speed time series (taking the first difference of the time series) is white noise, and 2) the presence of nonlinearity in the original wind speed time series. Nine data sets, including 10 min and hourly wind speed data, are examined. The results show that all of the differenced wind speed time series are correlated, and three out of the nine original wind speed time series satisfy the hypothesis of a linear stochastic generating process. It is concluded that for a specific wind speed time series, the nonlinearity is data-dependent from the perspective of practical time series analysis.

KeywordNonlinearity Surrogate Data Test Wind Forecasting White Noise
DOI10.1109/JPETS.2015.2424700
Language英语
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Document TypeConference paper
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
2.Department of System Engineering and Engineering Management, City University of Hong Kong, Hong Kong
3.State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha 410083, China
4.Faculty of Science and Technology, University of Macau, Macau, China
Recommended Citation
GB/T 7714
MIN GAN,HAN-XIONG LI,C. L. PHILIP CHEN,et al. A Potential Method for Determining Nonlinearity in Wind Data[C],2015:74-81.
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