China Rural Water and Hydropower. 2013, (2):
152-155.
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In order to more accurate and easy diagnosis of synchronous generator rotor winding inter-turn short-circuit fault, a novel fault diagnosis method is put forward, which based on Bayesian regularization back-propagation (BRBP) neural network. Measure and collect sample data in different fault-free operating conditions, including terminal parameters (voltage, active power, reactive power) and field current, then train a BRBP neural network model to predict field current. Input to the model with measured terminal parameters, and a predicted field current is obtained. Finally, compare the predicted field current with the corresponding measured field current, and a synchronous generator rotor winding inter-turn short-circuit fault is diagnosed when relative error exceeds a specific threshold. The micro-synchronous generator dynamic simulation results show that, the method is better accuracy than BP neural network method, only needs to simply set the BRBP neural network structures and parameters, is fast trained and easily applied to other synchronous generator, and is a effective rotor winding inter-turn short-circuit fault diagnosis method for synchronous generator.