A Hybrid Intelligent Excitation Control for Ship Power Station

Abstract:

Hybrid intelligent control technique is used for ship power station synchronous generator excitation control in this paper. The parameters and structure of the excitation controller are learned and adjusted through a hybrid learning algorithm combining self-organizing learning with BP learning. This algorithm converges much faster than original BP learning. Simulation results show that the synchronous generator excitation controller based on the learning algorithm can significantly stabilize terminal voltage.

• Periodical: Applied Mechanics and Materials (Volumes 128-129)
• Pages: 780-783
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.128-129.780
• Online since: October 2011
• Authors: Zhi Peng Shen, Chen Guo, Yu Ting Wang
• Keywords: BP Learning, Excitation Control, Hybrid Intelligent, Self-Organizing Learning

References

[1] Zhou Chang-jiu in: Hybrid intelligent fuzzy control and its application to double feet robot [D]. Dalian Maritime University, (1997).

[2] Chang Hong-Chan, Wang Mang-Hui in: Neural Network Based Self-Organizing Fuzzy Controller for Transient Stability of Multimachine Power Systems [J]. IEEE Trans. On Energy Conversion, 1995, 10(2): 1780-1785.

DOI: 10.1109/60.391901

[3] Srinivasan D, et al. in: Demand Forecasting Using Fuzzy Neural Computation with Special Emphasis on Weekend and Public Holiday Forecasting[J]. IEEE Trans. On Power Systems, 1995, 10(5): 1897-(1903).

DOI: 10.1109/59.476055

[4] C T Lin and C S G Lee. In: Neural-network-based fuzzy logic control and decision system [J]. IEEE Trans. Computer, 1991, 1320-1336.

DOI: 10.1109/12.106218

[5] Lin, C. T. and C. S. G. Lee. in: Neural fuzzy systems－A neuron-fuzzy synergism to intelligent systems[M], Prentice-Hall Int., Inc., (1996).