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.

Induction Motors Stator Fault Analysis based on Artificial Intelligence

2016 ◽  
Vol 2 (1) ◽  
pp. 69 ◽  
Author(s):  
Hussein Taha Hussein ◽  
Mohamed Ammar ◽  
Mohamed Moustafa Hassan
Keyword(s):  
Artificial Intelligence ◽  
Fault Detection ◽  
Short Circuit ◽  
Fault Analysis ◽  
Fault Detection And Diagnosis ◽  
Neuro Fuzzy ◽  
Detection Approach ◽  
Three Phase ◽  
Detection And Diagnosis ◽  
Stator Fault

This article presents a method for fault detection and diagnosis of stator inter-turn short circuit in three phase induction machines. The technique is based on the stator current and modelling in the dq frame using an Adaptive Neuro-Fuzzy artificial intelligence approach. The developed fault analysis method is illustrated using MATLAB simulations. The obtained results are promising based on the new fault detection approach.

scholarly journals THE ANALYSIS OF THE THREE-PHASE INDUCTION MOTOR WITH DEEP ROTOR BARS THROUGH NUMERICAL SIMULATIONS UTILIZING A MODIFIED MATHEMATICAL MODEL

2021 ◽  
Vol 2020 (1) ◽  
pp. 1-11
Author(s):  
Mihai IORDACHE ◽  
Sorin DELEANU ◽  
Neculai GALAN
Keyword(s):  
Mathematical Model ◽  
Numerical Simulations ◽  
Induction Motor ◽  
Skin Effect ◽  
Induction Machine ◽  
Rotor Resistance ◽  
Three Phase ◽  
Space Phasor ◽  
Analytical Expressions ◽  
Circuit Resistance

The three-phase induction machine mathematical model presented in the paper, is adequate for applying to the deep rotor bars case. The rotor resistance R’r(r), respectively its leakage inductivity L’r(r), depend upon the rotor currents’ frequency fr because of the skin effect. Following the previous considerations, one developed slip dependent analytical expressions of the rotor circuit resistance R’r(s), respectively rotor circuit leakage reactance L’r (s). A modified space phasor based mathematical model of the deep bar induction motor is tested through simulations to assess the motor’s characteristics. The results are in accordance with the literature.

Application of Probabilistic Neural Networks in Fault Diagnosis of Three-Phase Induction Motors

2013 ◽  
Vol 433-435 ◽  
pp. 705-708 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Probabilistic Neural Network ◽  
Short Circuit ◽  
Model And Simulation ◽  
Fast Training ◽  
Three Phase ◽  
Short Circuits ◽  
Stator Fault

In fault diagnosis of three-phase induction motors, traditional methods usually fail because of the complex system of three-phase induction motors. Short circuit is a very common stator fault in all the faults of three-phase induction motors. Probabilistic neural network is a kind of artificial neural network which is widely used due to its fast training and simple structure. In this paper, the diagnosis method based on probabilistic neural network is proposed to deal with stator short circuits. First, the principle and structure of probabilistic neural network is studied in this paper. Second, the method of fault setting and fault feature extraction of three-phase induction motors is proposed on the basis of the fault diagnosis of stator short circuits. Then the establishment of the diagnosis model based on probabilistic neural network is illustrated with details. At last, training and simulation tests are done for the model. And simulation results show that this method is very practical with its high accuracy and fast speed.

scholarly journals Real time observer-based stator fault diagnosis for IM

2020 ◽  
Vol 10 (1) ◽  
pp. 210
Author(s):  
Khadidja El Merraoui ◽  
Abdellaziz Ferdjouni ◽  
M’hamed Bounekhla
Keyword(s):  
Numerical Simulation ◽  
Real Time ◽  
Test Bench ◽  
Short Circuit ◽  
Optimization Method ◽  
Stator Winding ◽  
Tuning Parameters ◽  
Fault Severity ◽  
Im Model ◽  
Stator Fault

This paper proposes a delta connected IM model that takes the Stator winding Inter-Turn Short Circuit (SITSC) fault into account. In order to detect the fault and evaluate its severity, an observer based FDI method is suggested. It allows the generation of residual using extended Kalman filter (EKF). To overcome the problem of the EKF initialization, the cyclic optimization method is applied to determine its tuning parameters. The advantage of the proposed approach is the real-time quantification of the fault severity and the quick fault detection. Using numerical simulation under both the healthy and the faulty conditions, the proposed IM model and EKF-based FDI approach are confirmed. Experimental results obtained by a real-time implementation on test-bench validate the simulated results.

Steady-State and Dynamic Behavior of a Single-Phase Self-Excited Induction Generator Using a Three-Phase Machine

2007 ◽  
Vol 8 (3) ◽  
Author(s):  
S. N. Mahato ◽  
M. P. Sharma ◽  
S. P. Singh
Keyword(s):  
Steady State ◽  
Reference Frame ◽  
Single Phase ◽  
Short Circuit ◽  
Induction Machine ◽  
Transient Behavior ◽  
Experimental Results ◽  
Induction Generator ◽  
State Variables ◽  
Three Phase

This paper presents the steady-state and transient behavior of a single-phase self-excited induction generator (SEIG) using a three-phase machine with one shunt and one series excitation capacitors for resistive and inductive loads. The generation scheme consists of one three-phase delta connected induction machine and two capacitors - one connected in parallel with one winding and the other in series with a single-phase load. The dynamic model of the system has been developed as a hybrid model considering the stator phase currents in abc reference frame and the rotor currents in stationary d-q axes reference frame as state variables. The simulated and experimental results are presented for different dynamic conditions such as initiation of self-excitation, load perturbation and short-circuit. The simulated results of the steady-state analysis have been compared with the transient and experimental results and a close agreement between them indicates the accuracy and effectiveness of the approach.

scholarly journals Model Predictive Controller Utilized as an Observer for Inter-Turn Short Circuit Detection in Induction Machines - Preprint Version

2021 ◽  
Author(s):  
İlker Şahin ◽  
Ozan Keysan
Keyword(s):  
Short Circuit ◽  
Induction Machine ◽  
Main Idea ◽  
Optimization Procedure ◽  
Induction Machines ◽  
Voltage Source ◽  
Non Invasive ◽  
Finite Control ◽  
Predictive Controller ◽  
Stator Fault

<p>In this paper, a novel and non-invasive stator inter-turn short circuit (ITSC) online detection method is presented for an induction machine (IM), driven by a two-level voltage source inverter (2L-VSI) via finite control set model predictive control (FCS-MPC). The main idea of the proposed method is to utilize the controller itself as an observer: under the presence of a fault, the distribution of inverter switching states significantly deviates from the original balanced case. Therefore, by inspecting the inverter switching vectors, which are the outcomes of the FCS-MPC routine's online optimization procedure, a stator fault can be detected efficiently. It is observed that both the zero-vector allocation over the complex plane and the allocation among the active vectors are influenced by the presence of a stator short-circuit fault. The proposed fault detection strategy introduces little to no extra burden for processor and memory. Experimental results verify the proposed method, and inter-turn short circuits of two and three turns are confidently detected and located for a 500 W, two-pole IM.</p>

Three Phase Induction Motor's Stator Turns Fault Analysis Based on Artificial Intelligence

2017 ◽  
Vol 6 (3) ◽  
pp. 1-19 ◽  
Author(s):  
H. A. Taha Hussein ◽  
M. E. Ammar ◽  
M. A. Moustafa Hassan
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Short Circuit ◽  
Fault Analysis ◽  
Fault Detection And Diagnosis ◽  
Loading Conditions ◽  
Inference System ◽  
Fault Severity ◽  
Three Phase ◽  
Wide Range

This article presents a method for fault detection and diagnosis of stator inter-turn short circuit in three phase induction machines. The technique is based on modelling the motor in the dq frame for both health and fault cases to facilitate recognition of motor current. Using an Adaptive Neuro-Fuzzy Inference System (ANFIS) to provide an efficient fault diagnosis tool. An artificial intelligence network determines the fault severity values using the stator current history. The performance of the developed fault analysis method is investigated using Matlab/Simulink® software. Stator turns faults are detected through current monitoring of a 2 Hp three phase induction motor under various loading conditions. Fault history is calculated under various loading conditions, and a wide range of fault severity.

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