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RBFNN for fault diagnosis of rotor windings inter-turn short circuit in turbine-Generator

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3 Author(s)
Zhao Yan-jun ; School of Electrical Engineering, North China Electric Power University, Baoding 071003, China ; Li Yong-gang ; Hu Ji-wei

The electromagnetic characteristic and rotor vibration characteristic of turbine-generator are analyzed when rotor windings inter-turn short circuit fault has happened. This paper also gets relevant characteristic parameters. Based on characteristic parameters, RBFNN (radial basis function neural network) can be adequately trained and diagnosis rotor windings inter-turn short circuit. RBFNN is independent on mathematic models and parameters of turbine-generator. Finally practically acquired dynamic experiment data of the MJF-30-6 generator, the results of verification show that the theory analysis is right and the RBFNN can diagnosis rotor fault and estimate fault turns ratio.

Published in:

Condition Monitoring and Diagnosis, 2008. CMD 2008. International Conference on

Date of Conference:

21-24 April 2008