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A nonlinear two‐cluster Gaussian mixture scenario model for wind power
Author(s) -
Chen Shaonan,
Chen Biyun,
Wei Hua
Publication year - 2016
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22272
Subject(s) - mixture model , gaussian , wind power , nonlinear system , wind speed , cluster (spacecraft) , expectation–maximization algorithm , power (physics) , turbine , flexibility (engineering) , computer science , mathematical optimization , mathematics , control theory (sociology) , algorithm , meteorology , statistics , engineering , artificial intelligence , physics , maximum likelihood , aerospace engineering , control (management) , quantum mechanics , electrical engineering , programming language
To overcome the low precision and poor flexibility of the wind power scenario model, this paper proposes a nonlinear two‐cluster Gaussian mixture scenario model for wind power based on the expectation maximization (EM) algorithm according to Wasserstein optimal scenario theory. First, the EM algorithm is used to classify the wind speed data and establish the Gaussian mixture model (GMM). Second, the Wasserstein distance scenarios of two Gaussian distributions with different parameters are calculated based on wind speed data and nonlinear wind turbine power curve, respectively. Finally, a cross combination of the two scenarios is used to obtain the nonlinear two‐cluster mixture scenario model with a mixture Gaussian probability parameters. The obtained results show that the nonlinear two‐cluster Gaussian mixture scenario model is applicable not only to the regular wind speed probability distribution but also to that with irregular and two‐peak characteristics. Moreover, the generated energy deviation can be controlled within 2.5%. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.