A Rotor Winding Inter-turn Short-circuit Fault Diagnosis Method Based on BRBP Neural Networks

In order to diagnose synchronous generator rotor winding inter-turn short-circuit fault more accurately and easily,a novel fault diagnosis method is put forward,based on Bayesian regularization back-propagation(BRBP) neural networks.Sample data under different fault-free operating conditions are measured and collected,including terminal parameters(voltage,active power,reactive power) and field currents,then a BRBP neural network model is established to predict field currents.Input to the model with measured terminal parameters,and a predicted field current is obtained.Finally,the predicted field currents are compared with the corresponding measured field currents,and a synchronous generator rotor winding inter-turn short-circuit fault is diagnosed when relative error exceeds a specific threshold.The dynamic simulation results of micro-synchronous generator show that,the method is better.It is easily applied to other synchronous generators,and is an effective rotor winding inter-turn short-circuit fault diagnosis method for synchronous generators.