The Modeling of Steam Turbine Speed Control System Based on Radial Basis Function Neural Network
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.
Liangchi Zhang, Chunliang Zhang and Tielin Shi
Y. Ren et al., "The Modeling of Steam Turbine Speed Control System Based on Radial Basis Function Neural Network", Advanced Materials Research, Vols. 139-141, pp. 1822-1826, 2010