The Modeling of Steam Turbine Speed Control System Based on Radial Basis Function Neural Network

Yong Ren; Tao Yang; Wei Gao; Yang Hai Li

doi:10.4028/www.scientific.net/AMR.139-141.1822

Paper Titles

The Study of Soccer Robot Path Planning Based on Grid-Based Potential Field Method Improvements
p.1798

Study on Mathematical Modeling Method for Photoelectric Stable Platform
p.1803

Precision Construction in Japan - Utilization of Information and Communication Technology
p.1807

Design of Frequency Control System for Centrifuge in Multi-Parameter Combined Environmental Testing Device
p.1817

The Modeling of Steam Turbine Speed Control System Based on Radial Basis Function Neural Network
p.1822

Modeling and Stability Analysis of Multi-Rate Networked Control Systems with Multiple Control Loops
p.1827

Development and Application of Capacitor Compensation AC De-Icing Equipment
p.1834

Design and Experimental Study of a Novel Fault Current Limiter
p.1839

A Modified Quality Control Method for Manufacturing Process in Mask Industry
p.1843

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 139-141The Modeling of Steam Turbine Speed Control System...

The Modeling of Steam Turbine Speed Control System Based on Radial Basis Function Neural Network

Abstract:

The model of speed control system plays an important role in power system stability studies, the non-linear properties prevent us from getting accurate mechanism model. In this paper, the radial basis function neural network with self-structuring and fast convergence is used in the modeling of steam turbine speed control system in the modeling process, also, this paper presents a method which combines particle swarm optimization algorithm and least-squares algorithm for the neural network’s training, it has the property of high accuracy and fast convergence, after training, the proposed model and related training algorithm are verified by the test data of one power plant, it has proved that the neural network can be used in the modeling of the speed control system for the power system stability studies.

You might also be interested in these eBooks

Manufacturing Engineering and Automation I

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 139-141)

Pages:

1822-1826

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.139-141.1822

Citation:

Cite this paper

Online since:

October 2010

Authors:

Yong Ren, Tao Yang, Wei Gao, Yang Hai Li

Keywords:

Least Square Algorithm, Particle Swarm Optimization Algorithm (PSO), RBF Neural Network, Speed Control System, Steam Turbine

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Q. Zhang; P.L. So: Dynamic modeling of a combined cycle plant for power system stability studies. Power Engineering Society Winter Meeting, 2000, Vol. 2, pp.1538-1543.

DOI: 10.1109/pesw.2000.850211

Google Scholar

[2] Z.Q. Wang; X.Q. Chen and C.D. Deng: The Power System Load Modeling Based on Recurrent RBF Neural Network. International Power Engineering Conference, 2007, pp.910-915.

Google Scholar

[3] K.Y. Lee, J.S. Heo, and J.A. Hoffman: Neural Network-Based Modeling for A Large-Scale Power Plant. Power Engineering Society General Meeting, 2007, pp.1-8.

DOI: 10.1109/pes.2007.385506

Google Scholar

[4] A. Keyhani, W. Lu, and G.T. Heydt: Composite Neural Network Load Models for Power System Stability Analysis. Power Systems Conference and Exposition. 2004, Vol. 2, p.11591163.

DOI: 10.1109/psce.2004.1397702

Google Scholar

[5] T. Poggio, F. Girosi: Networks for Approximation and Learning. Proceeding of the IEEE, Vol. 78(1990) No. 9, pp: 1481-1497.

DOI: 10.1109/5.58326

Google Scholar

[6] J. Kennedy, R. Eberhart: Particle swarm optimization. Proc. IEEE International Conference on Neural Networks(Perth, Australia, 1995), pp: 1942-(1948).

Google Scholar