Neuron-Adaptive PID Based Speed Control of SCSG Wind Turbine System
Joint Authors
Zuo, Shan
Zhou, Zheng
Wang, Lei
Song, Yongduan
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-05-19
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
In searching for methods to increase the power capacity of wind power generation system, superconducting synchronous generator (SCSG) has appeared to be an attractive candidate to develop large-scale wind turbine due to its high energy density and unprecedented advantages in weight and size.
In this paper, a high-temperature superconducting technology based large-scale wind turbine is considered and its physical structure and characteristics are analyzed.
A simple yet effective single neuron-adaptive PID control scheme with Delta learning mechanism is proposed for the speed control of SCSG based wind power system, in which the RBF neural network (NN) is employed to estimate the uncertain but continuous functions.
Compared with the conventional PID control method, the simulation results of the proposed approach show a better performance in tracking the wind speed and maintaining a stable tip-speed ratio, therefore, achieving the maximum wind energy utilization.
American Psychological Association (APA)
Zuo, Shan& Song, Yongduan& Wang, Lei& Zhou, Zheng. 2014. Neuron-Adaptive PID Based Speed Control of SCSG Wind Turbine System. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1033718
Modern Language Association (MLA)
Zuo, Shan…[et al.]. Neuron-Adaptive PID Based Speed Control of SCSG Wind Turbine System. Abstract and Applied Analysis No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1033718
American Medical Association (AMA)
Zuo, Shan& Song, Yongduan& Wang, Lei& Zhou, Zheng. Neuron-Adaptive PID Based Speed Control of SCSG Wind Turbine System. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1033718
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1033718