Filtering Based Maximum Likelihood Stochastic Gradient Prediction on Wind Power Curtailment

Authors

Ziyun Wang, Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education),College of the Internet of Things, Jiangnan University, Wuxi 214122, China;
Zhicheng Ji, Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education),College of the Internet of Things, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: The parameter estimation problem of Hammerstein finite impulse response models and its application on the wind curtailment prediction field were considered. By adopting the maximum likelihood principle, the maximum likelihood estimate was obtained by minimizing the likelihood function. To reduce the impact of the unknown noise term, the maximum likelihood idea and the filtering theory were combined by changing the coupled nonlinear model into a parameter-independent model and to derive a filtering based maximum likelihood stochastic gradient algorithm for the Hammerstein system modeling on wind power curtailment prediction. The given simulation validates that the proposed algorithm can identify the wind power characteristic curve accurately and contributes to calculate the wind power curtailment prediction that shows its good practicability.

Recommended Citation

Wang, Ziyun and Ji, Zhicheng (2020) "Filtering Based Maximum Likelihood Stochastic Gradient Prediction on Wind Power Curtailment," Journal of System Simulation: Vol. 29: Iss. 3, Article 17.
DOI: 10.16182/j.issn1004731x.joss.201703017
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss3/17

First Page

589

Revised Date

2017-01-03

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201703017

Last Page

594

CLC

TP13

Recommended Citation

Wang Ziyun, Ji Zhicheng. Filtering Based Maximum Likelihood Stochastic Gradient Prediction on Wind Power Curtailment[J]. Journal of System Simulation, 2017, 29(3): 589-594.

DOI

10.16182/j.issn1004731x.joss.201703017