A nonlinear two‐cluster Gaussian mixture scenario model for wind power. Issue 5 (13th July 2016)

• Record Type: Journal Article
• Authors: Chen, Shaonan; Chen, Biyun; Wei, Hua
• Is Part Of: IEEJ transactions on electrical and electronic engineering. Volume 11:Issue 5(2016:Sep.)
• Page Start: 549
• Page End: 555
• Publication Date: 2016-07-13
• Subjects: EM algorithm -- Gaussian mixture model -- wind power -- wind speed probability distribution -- Wasserstein optimal scenario; Electrical engineering -- Periodicals; Electronics -- Periodicals; 621.3
• DOI: 10.1002/tee.22272
• Languages: English
• ISSNs: 1931-4973

Abstract

Abstract : 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.