Condition Prediction of Hydroelectric Generating Unit Based on Immune Optimized RBFNN

Authors:
Zhong Liu;Shuyun Zou;Shuangquan Liu;Fenghua Jin;Xuxiang Lu
Affiliations:
Changsha University of Science and Technolgy, Changsha, China 410076;Changsha University of Science and Technolgy, Changsha, China 410076;Huazhong University of Science and Technolgy, Wuhan, China 430074;Changsha University of Science and Technolgy, Changsha, China 410076;Changsha University of Science and Technolgy, Changsha, China 410076
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Year:
2008

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Abstract

Establishing the condition prediction model of characteristic parameters is one of the key parts in the implementation of condition based maintenance (CBM) of the hydroelectric generating unit (HGU). The performance of radial basis function neural network (RBFNN) in prediction mainly depends on the determination of the number and locations of data centers at the hidden layer. A novel approach inspired from the immune optimization principles is proposed in this paper and used to determine and optimize the structure at the hidden layer. The immune optimized RBFNN has been applied to the vibration condition prediction of the hydroturbine guiding bearing. The prediction results are compared with those by some other intelligent algorithms and the actual values, which shows the effectiveness and the preciseness of the proposed immune optimized RBFNN.