Open AccessFault Diagnosis Based on ANN for Turn-to-Turn Short Circuit of Synchronous Generator Rotor Windings
Author(s) -
H. Z.,
Leping Pu
Publication year - 2009
Publication title -
journal of electromagnetic analysis and applications
Language(s) - English
Resource type - Journals
eISSN - 1942-0749
pISSN - 1942-0730
DOI - 10.4236/jemaa.2009.13028
Subject(s) - fault (geology) , rotor (electric) , generator (circuit theory) , computer science , electromagnetic coil , short circuit , shunt generator , turn (biochemistry) , terminal (telecommunication) , control theory (sociology) , power (physics) , induction generator , voltage , excitation , permanent magnet synchronous generator , electrical engineering , engineering , physics , artificial intelligence , telecommunications , control (management) , nuclear magnetic resonance , quantum mechanics , seismology , geology
Rotor winding turn-to-turn short circuit is a common electrical fault in steam turbines. When turn-to-turn short circuit fault happens to rotor winding of the generator, the generator terminal parameters will change. According to these parameters, the conditions of the rotor winding can be reflected. However, it is hard to express the relations between fault information and generator terminal parameters in accurate mathematical formula. The satisfactory results in fault diagnosis can be obtained by the application of neural network. In general, the information about the severity level of the generator faults can be acquired directly when the faulty samples are found in the training samples of neural network. However, the faulty samples are difficult to acquire in practice. In this paper, the relations among active power, reactive power and excitation current are discovered by analyzing the generator mmf with terminal voltage constant. Depending on these relations, a novel diagnosis method of generator rotor winding turn-to-turn short circuit fault is proposed by using ANN method to obtain the fault samples directly, without destructive tests