This work proposes an alternative strategy to the use of a speed sensor in <span style="white-space:normal;font-size:10pt;font-family:;" "="">the implementation of active and reactive po...This work proposes an alternative strategy to the use of a speed sensor in <span style="white-space:normal;font-size:10pt;font-family:;" "="">the implementation of active and reactive power based model reference adaptive system (PQ-MRAS) estimator in order to calculate the rotor and stator resistances of an induction motor (IM) and the use of these parameters for the detection of inter-turn short circuits (ITSC) faults in the stator of this motor. The rotor and stator resistance estimation part of the IM is performed by the PQ-MRAS method in which the rotor angular velocity is reconstructed from the interconnected high gain observer (IHGO). The ITSC fault detection part is done by the derivation of stator resistance estimated by the PQ-</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">MRAS estimator. In addition to the speed sensorless detection of ITSC faults of the IM, an approach to determine the number of shorted turns based on the difference between the phase current of the healthy and faulty machine is proposed. Simulation results obtained from the MATLAB/Simulink platform have shown that the PQ-MRAS estimator using an interconnected high-</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">gain observer gives very similar results to those using the speed sensor. The </span><span style="white-space:normal;font-size:10pt;font-family:;" "="">estimation errors in the cases of speed variation and load torque are al</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">mos</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">t identical. Variations in stator and rotor resistances influence the per</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">formance of the observer and lead to poor estimation of the rotor resistance. The results of ITSC fault detection using IHGO are very similar to the results in the literature using the same diagnostic approach with a speed sensor.</span>展开更多
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. ...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.展开更多
文摘This work proposes an alternative strategy to the use of a speed sensor in <span style="white-space:normal;font-size:10pt;font-family:;" "="">the implementation of active and reactive power based model reference adaptive system (PQ-MRAS) estimator in order to calculate the rotor and stator resistances of an induction motor (IM) and the use of these parameters for the detection of inter-turn short circuits (ITSC) faults in the stator of this motor. The rotor and stator resistance estimation part of the IM is performed by the PQ-MRAS method in which the rotor angular velocity is reconstructed from the interconnected high gain observer (IHGO). The ITSC fault detection part is done by the derivation of stator resistance estimated by the PQ-</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">MRAS estimator. In addition to the speed sensorless detection of ITSC faults of the IM, an approach to determine the number of shorted turns based on the difference between the phase current of the healthy and faulty machine is proposed. Simulation results obtained from the MATLAB/Simulink platform have shown that the PQ-MRAS estimator using an interconnected high-</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">gain observer gives very similar results to those using the speed sensor. The </span><span style="white-space:normal;font-size:10pt;font-family:;" "="">estimation errors in the cases of speed variation and load torque are al</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">mos</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">t identical. Variations in stator and rotor resistances influence the per</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">formance of the observer and lead to poor estimation of the rotor resistance. The results of ITSC fault detection using IHGO are very similar to the results in the literature using the same diagnostic approach with a speed sensor.</span>
文摘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.