A fault warning for inter-turn short circuit of excitation winding of synchronous generator based on GRU-CNN

Synchronous generators are important components of power systems and are necessary to maintain its normal and stable operation.To perform the fault diagnosis of mild inter-turn short circuit in the excitation winding of a synchronous generator,a gate recurrent unit-convolutional neural network(GRU-CNN)model whose structural parameters were determined by improved particle swarm optimization(IPSO)is proposed.The outputs of the model are the excitation current and reactive power.The total offset distance,which is the fusion of the offset distance of the excitation current and offset distance of the reactive power,was selected as the fault judgment criterion.The fusion weights of the excitation current and reactive power were determined using the anti-entropy weighting method.The fault-warning threshold and fault-warning ratio were set according to the normal total offset distance,and the fault warning time was set according to the actual situation.The fault-warning time and fault-warning ratio were used to avoid misdiagnosis.The proposed method was verified experimentally.

A Fault Feature Extraction Model in Synchronous Generator under Stator Inter-Turn Short Circuit Based on ACMD and DEO3S

This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fault,the sta-tor vibration signal analysis based on ACMD(adaptive chirp mode decomposition)and DEO3S(demodulation energy operator of symmetrical differencing)was adopted to extract the fault feature.Firstly,FT(Fourier trans-form)is applied to the vibration signal to obtain the instantaneous frequency,and PE(permutation entropy)is calculated to select the proper weighting coefficients.Then,the signal is decomposed by ACMD,with the instan-taneous frequency and weighting coefficient acquired in the former step to obtain the optimal mode.Finally,DEO3S is operated to get the envelope spectrum which is able to strengthen the characteristic frequencies of the stator inter-turn short circuit fault.The study on the simulating signal and the real experiment data indicates the effectiveness of the proposed method for the stator inter-turn short circuit fault in synchronous generators.In addition,the comparison with other methods shows the superiority of the proposed model.

The Detection of Inter-Turn Short Circuits in the Stator Windings of Sensorless Operating Induction Motors

This work proposes an alternative strategy to the use of a speed sensor in 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-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-gain observer gives very similar results to those using the speed sensor. The estimation errors in the cases of speed variation and load torque are almost identical. Variations in stator and rotor resistances influence the performance 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.

Simplified mathematical model of inter-turn short circuit of field windings in hydro-generators and its application

Inter-turn short circuit of field windings is a common electrical fault of generators.Simulation is an important method of investigating the fault and providing data support for fault monitoring.However,huge numbers of pole pairs and damper loops in large hydro-generators would lead to lengthy calculation time,hindering scientific research and engineering application.To deal with this problem,we analyze a theoretical basis for a damper winding simplified model and then propose an equivalent treatment method.Through the analysis of steady-state current harmonic characteristics of generators with different stator winding configurations during the fault,the simplified models suitable for steady-state calculation are derived from two aspects,namely,additional rotor harmonic current frequency characteristics and the relationship of the amplitude as well as the phase of each branch current of the stator.The calculation and experimental results of the two simplified models are then compared to verify the models' correctness.A calculation example of the Three Gorges left bank VGS generator shows few deviations between the calculation results of the simplified model and the original model.Moreover,the calculation time using the simplified model is 1/1500 that using the original model,which provides a more effective tool for on-line fault monitoring.Finally,the sensitivity-verification application of the fault-monitoring scheme based on the stator steady-state unbalanced current RMS is depicted.The result shows that the scheme can monitor two-turn short circuits of field windings in the Three Gorges generator and provide high sensitivity.

Application of Morphological Max-Lifting Scheme for Identification of Induction Motor Stator Inter-turn Short Circuit

The harmonic components of stator winding current in induction motor will change under the condition of stator inter-turn short circuit.According to these characteristics,in this paper,a novel technique based on morphological maxlifting scheme is proposed for identification of induction motor stator inter-turn short circuit.A max-lifting scheme is applied to process stator winding currents to extract these characteristics.An indicator,r,is computed to identify the short circuit.The transient model of induction motor is employed to simulate oneturn to six-turn stator inter-turn short circuits in an induction motor.Extensive simulation work has been conducted under normal conditions,abnormal conditions(voltage imbalance and varying load),stator inter-turn short circuit conditions,and conditions of any combinations of the above.The results have shown that the scheme proposed in this paper has a high identification rate for induction motor stator inter-turn short circuit.

Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

A novel approach by introducing a statistical parameter to estimate the severity of incipient stator inter-turn short circuit(ITSC)faults in induction motors(IMs)is proposed.Determining the incipient ITSC fault and its severity is challenging for several reasons.The stator currents in the healthy and faulty cases are highly similar during the primary stage of the fault.Moreover,the conventional statistical parameters resulting from the analysis of fault signals do not consistently show a systematic variation with respect to the increase in fault intensity.The objective of this study is the early detection of incipient ITSC faults.Furthermore,it aims to determine the percentage of shorted turns in the faulty phase,which acts as an indicator for severe damage to the stator winding.Modeling of the motor in healthy and defective cases is performed using the Clarke Concordia transform.A discrete wavelet transform is applied to the motor currents using a Daubechies-8 wavelet.The statistical parameters L1 and L2 norms are computed for the detailed coefficients.These parameters are obtained under a variety of loads and defects to acquire the most accurate and generalized features related to the fault.Combining L1 and L2 norms creates a novel statistical parameter with notable characteristics to achieve the research aim.An artificial neural network-based back propagation algorithm is employed as a classifier to implement the classification process.The classifier output defines the percentage of defective turns with a high level of accuracy.The competency of the adopted methodology is validated via simulations and experiments.The results confirm the merits of the proposed method,with a classification test correctness of 95.29%.