Double-fed three-phase induction machine model for simulation of inter-turn short circuit fault

2009 ◽  
Author(s):  
A. Yazidi ◽  
H. Henao ◽  
G.A. Capolino ◽  
L. Capocchi ◽  
D. Federici
Keyword(s):  
Short Circuit ◽  
Induction Machine ◽  
Machine Model ◽  
Three Phase ◽  
Short Circuit Fault

scholarly journals An accurate inter-turn short circuit faults model dedicated to induction motors

2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Fatima Babaa ◽  
Ouafae Bennis
Keyword(s):  
Induction Motors ◽  
Model Simulation ◽  
Short Circuit ◽  
Induction Machine ◽  
Industrial Systems ◽  
Proposed Model ◽  
Three Phase ◽  
Fault Prognosis ◽  
Short Circuits ◽  
Short Circuit Fault

Safety, disponibility and continuity of industrial systems are major issue in maintenance. In the last decades, these points are the important axes in the field of research. In fact, in many industrial processes research has picked up a fervent place and a particular importance in the area of fault diagnosis of electrical machines, in fact, a fault prognosis has become almost indispensable. The need of a mathematical model of three-phase induction machine, suitable for the simulation of machines behaviour under fault conditions, has received considerable attention. The paper presents a new practical and more precise model for induction motors after introducing inter turn short circuits faults. The proposed model is based on coupled magnetic circuit theory, capable to take into account any electrical asymmetry conditions. To verify the exactitude and the effectiveness of the model, simulation results for induction machine under interturn short circuit fault are presented. In spite of its simplicity, the proposed model is able to provide useful indications for diagnostic purposes. Experimental study is presented at the end of the paper to show that the proposed model predicts the induction machine behavior with a good accuracy.

Induction machine stator short-circuit fault detection using support vector machine

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Saddam Bensaoucha ◽  
Youcef Brik ◽  
Sandrine Moreau ◽  
Sid Ahmed Bessedik ◽  
Aissa Ameur
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Short Circuit ◽  
Induction Machine ◽  
Support Vector ◽  
Data Set ◽  
Content Type ◽  
Three Phase ◽  
Learning Machine ◽  
Short Circuit Fault

Purpose This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases. Design/methodology/approach To evaluate the performance of the SVM, three supervised algorithms of machine learning, namely, multi-layer perceptron neural networks (MLPNNs), radial basis function neural networks (RBFNNs) and extreme learning machine (ELM) are used along with the SVM in this study. Thus, all classifiers (SVM, MLPNN, RBFNN and ELM) are tested and the results are compared with the same data set. Findings The obtained results showed that the SVM outperforms MLPNN, RBFNNs and ELM to diagnose the health status of the IM. Especially, this technique (SVM) provides an excellent performance because it is able to detect a fault of two short-circuited turns (early detection) when the IM is operating under a low load. Originality/value The original of this work is to use the SVM algorithm based on the phase shift between the stator currents and their voltages as inputs to detect and locate the ITSC fault.

scholarly journals Modeling and Evaluation of Stator and Rotor Faults for Induction Motors

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 133 ◽  
Author(s):  
Jing Tang ◽  
Jie Chen ◽  
Kan Dong ◽  
Yongheng Yang ◽  
Haichen Lv ◽  
...  
Keyword(s):  
Short Circuit ◽  
Induction Machine ◽  
Load Test ◽  
Discrete Models ◽  
Digital Implementation ◽  
Rotor Faults ◽  
Fault Severity ◽  
Three Phase ◽  
Stator Fault ◽  
Circuit Resistance

The modeling of stator and rotor faults is the basis of the development of online monitoring techniques. To obtain reliable stator and rotor fault models, this paper focuses on dynamic modeling of the stator and rotor faults in real-time, which adopts a multiple-coupled-circuit method by using a winding function approach for inductance calculation. Firstly, the model of the induction machine with a healthy cage is introduced, where a rotor mesh that consists of a few rotor loops and an end ring loop is considered. Then, the stator inter-turn fault model is presented by adding an extra branch with short circuit resistance on the fault part of a stator phase winding. The broken rotor bar fault is then detailed by merging and removing the broken-bar-related loops. Finally, the discrete models under healthy and faulty conditions are developed by using the Tustin transformation for digital implementation. Moreover, the stator and rotor mutual inductances are derived as a function of the rotor position according to the turn and winding functions distribution. Simulations and experiments are performed on a 2.2-kW/380-V/50-Hz three-phase and four-pole induction motor to show the performance of the stator and rotor faults, where the saturation effect is considered in simulations by exploiting the measurements of a no load test. The simulation results are in close agreement with the experimental results. Furthermore, magnitudes of the characteristic frequencies of 2f1 in torque and (1 ± 2s)f1 in current are analyzed to evaluate the stator and rotor fault severity. Both indicate that the stator fault severity is related to the short circuit resistance. Further, the number of shorted turns and the number of continuous broken bars determines the rotor fault severity.

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