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

Abstract-DC microgrid contains a large number of tightly regulated closed-loop converters, which performance for the constant power loads and may cause stability problem of bus voltage when used as a load since they trend to draw constant power. In order to improve the stability of DC microgrid, this paper proposes a method for stabilization the DC microgrid based on the state feedback. By introducing the state feedback signal, this method increases the damping of the system and ensures the stable operation of the whole DC microgrid. The feasibility and effectiveness of this method are verified by the simulation results.