Journal articles: 'Stator short circuit faults'

1

Zhang, Ming, Yi Ming Zhang, and Jiang Tao Tong. "Research on Stator Winding Inter Turn Short-Circuit Faults of Aeronautical Fault-Tolerant Machine Based on Maxwell 2D." Applied Mechanics and Materials 130-134 (October 2011): 119–23. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.119.

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Stator winding inter turn short-circuit fault is one of the most common internal faults of fault-tolerant machine, which can disconnect the fault phases and keep operating correctly in the event of a failure. Stator winding short-circuit fault model is established through analysis. Based on finite element method, the high-power density fault-tolerant machine internal magnetic field simulation and analysis is implemented using Maxwell2D and induced voltage frequency spectrum is analyzed by FFT method. The characteristics of stator winding short-circuit faults are summarized, which lay a solid foundation for fault-tolerant machine earlier faults prediction and winding switching.

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Aubert, Brice, Jérémi Régnier, Stéphane Caux, and Dominique Alejo. "Stator Winding Fault Diagnosis in Permanent Magnet Synchronous Generators Based on Short-Circuited Turns Identification Using Extended Kalman Filter." ACTA IMEKO 3, no. 4 (December 1, 2014): 4. http://dx.doi.org/10.21014/acta_imeko.v3i4.146.

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<p class="Abstract">This paper deals with an Extended Kalman Filter based fault detection for inter-turn short-circuit in Permanent Magnet Synchronous Generators. Inter-turn short-circuits are among the most critical faults in the PMSG. Indeed, due to permanent magnets, the short-circuit current is maintained as long as the machine is rotating. Thus, a specific faulty model in d-q frame is developed to estimate the number of short-circuited turns which are used to build a fault indicator. Simulation results demonstrate the sensitivity and the robustness of the proposed fault indicator against various operation points on an electrical network even for a few number of short-circuited turns.</p>

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Liang, Hong, Yong Chen, Siyuan Liang, and Chengdong Wang. "Fault Detection of Stator Inter-Turn Short-Circuit in PMSM on Stator Current and Vibration Signal." Applied Sciences 8, no. 9 (September 16, 2018): 1677. http://dx.doi.org/10.3390/app8091677.

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The stator inter-turn short circuit fault is one of the most common and key faults in permanent magnet synchronous motor (PMSM). This paper introduces a time–frequency method for inter-turn fault detection in stator winding of PMSM using improved wavelet packet transform. Both stator current signal and vibration signal are used for the detection of short circuit faults. Two different experimental data from a three-phase PMSM were processed and analyzed by this time–frequency method in LabVIEW. The feasibility of this approach is shown by the experimental test.

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Tang, Jing, Jie Chen, Kan Dong, Yongheng Yang, Haichen Lv, and Zhigang Liu. "Modeling and Evaluation of Stator and Rotor Faults for Induction Motors." Energies 13, no. 1 (December 26, 2019): 133. http://dx.doi.org/10.3390/en13010133.

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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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Alawady, A. A., M. F. M. Yousof, N. Azis, and M. A. Talib. "Frequency response analysis technique for induction motor short circuit faults detection." International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no. 3 (September 1, 2020): 1653. http://dx.doi.org/10.11591/ijpeds.v11.i3.pp1653-1659.

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<p>The paper presents the description for diagnostic methods of induction motor's stator windings fault. The presented methods use Frequency Response Analysis (FRA) technique for detection of Winding Faults in Induction Motor . This method is previously reliable method for faults diagnosis and detection in many parts of transformers including transformer windings. In this paper, this method was used for motor windings faults detection. This paper presents the FRA response interpretation on internal short circuit (SC) fault at stator winding on three cases studies of different three-phase induction motors (TPIM), were analysed according to two status: healthy induction motor at normal winding status and same motor with windings shorted of main windings. A conclusion of this paper provides the interpretation of and validation the FRA response due to internal SC fault case by using NCEPRI algorithm, which is considered as one of certified statistical indicators. The proposed method in this paper had a useful result for detect and diagnosis of stator windings faults of TPIM. The applications of developed method can be used to detece the other machines types faults.</p>

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Puzakov, Andrey. "Diagnosing of automotive alternators on thermal state." MATEC Web of Conferences 298 (2019): 00005. http://dx.doi.org/10.1051/matecconf/201929800005.

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Since automotive alternators serve as the primary sources of power onboard of vehicles, the online diagnostics of technical conditions thereof is a relevant task. An advantage of temperature as a diagnostic parameter is sensitivity to most faults at the early stage of their development. Physical modeling of faults (stator one-line open fault, stator turn-to-turn short circuit, stator winding phase-to-phase short circuit, circuit opening and short circuit of rectifier diodes) has been done by forced increase (decrease) of electrical resistance between alternator elements. In order to measure alternator temperature, it has been brought to steady thermal state within 20 minutes. It has been found that the alternator temperature in case of faults can increase the rated temperature by 10-30 °С even when the alternator operates without load. An algorithm has been developed to find alternator faults by evaluating the thermal state thereof, which can become a basis of an onboard automatic online diagnosing system of an automotive alternator.

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Głowacz, Z., and J. Kozik. "Detection of Synchronous Motor Inter-Turn Faults Based on Spectral Analysis of Park’S Vector / Detekcja Zwarc Zwojowych W Silniku Synchronicznym Bazujaca Na Analizie Spektralnej Wektora Przestrzennego Pradu Twornika." Archives of Metallurgy and Materials 58, no. 1 (March 1, 2013): 19–23. http://dx.doi.org/10.2478/v10172-012-0144-y.

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This paper describes detection of synchronous motor inter-turn faults based on symptoms contained in stator phase currents. Armature short circuit, caused by insulation degradation are quite commonly occurring defects in electrical machines. Initially, short circuit comprises mostly single coils, causing the temperature rise due to higher value of current, which can reach up to tens times of the rated value. At the same time the phase current does not increase significantly. Increased temperature leads to rapid damage of the insulation and shorting the adjacent coils spreading the fault to the entire winding in a short time. Thus, it is very important to detected this type of fault in its early stage. Unfortunately currently used motor protection devices are insensitive to short-circuits of a small number of turns, because they cause too small quantitative changes in the phase currents. Phase currents begin to rise to the level detectable by protection devices when a large part of the winding is already covered by a fault. Therefore, there is a need for research on diagnostics of this type of damage. For the purpose of this paper a stepped short circuit fault of one coil group in the stator phase winding is performed. Shorting resistance values are chosen so that the short fault is diagnosed in its early stage. Spectral analysis of stator phase currents is carried out followed by spectral analysis of stator currents Park’s vector. Comparison of the results of both studies shows that the signal of stator current Park’s vector is more suitable in diagnostics of this type of faults.

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Fadzail, N. F., S. Mat Zali, M. A. Khairudin, and N. H. Hanafi. "Stator winding fault detection of induction generator based wind turbine using ANN." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 1 (July 1, 2020): 126. http://dx.doi.org/10.11591/ijeecs.v19.i1.pp126-133.

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This paper presents a stator winding faults detection in induction generator based wind turbines by using artificial neural network (ANN). Stator winding faults of induction generators are the most common fault found in wind turbines. This fault may lead to wind turbine failure. Therefore, fault detection in induction generator based wind turbines is vital to increase the reliability of wind turbines. In this project, the mathematical model of induction generator based wind turbine was developed in MATLAB Simulink. The value of impedance in the induction generators was changed to simulate the inter-turn short circuit and open circuit faults. The simulated responses of the induction generators were used as inputs in the ANN model for fault detection procedures. A set of data was taken under different conditions, i.e. normal condition, inter-turn short circuit and open circuit faults as inputs for the ANN model. The target outputs of the ANN model were set as ‘0’ or ‘1’, based on the fault conditions. Results obtained showed that the ANN model can detect different types of faults based on the output values of the ANN model. In conclusion, the stator winding faults detection procedure for induction generator based wind turbines by using ANN was successfully developed.

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Abdullateef, A. I., O. S. Fagbolagun, M. F. Sanusi, M. F. Akorede, and M. A. Afolayan. "Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction Motor." Journal of Applied Sciences and Environmental Management 24, no. 3 (April 23, 2020): 417–24. http://dx.doi.org/10.4314/jasem.v24i3.3.

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Induction motors are the backbone of the industries because they are easy to operate, rugged, economical and reliable. However, they are subjected to stator’s faults which damage the windings and consequently lead to machine failure and loss of revenue. Early detection and classification of these faults are important for the effective operation of induction motors. Stators faults detection and classification based on wavelet Transform was carried out in this study. The feature extraction of the acquired data was achieved using lifting decomposition and reconstruction scheme while Euclidean distance of the Wavelet energy was used to classify the faults. The Wavelet energies increased for all three conditions monitored, normal condition, inter-turn fault and phase-to-phase fault, as the frequency band of the signal decreases from D1 to A3. The deviations in the Euclidean Distance of the current of the Wavelet energy obtained for the phase-to-phase faults are 99.1909, 99.8239 and 87.9750 for phases A and B, A and C, B and C respectively. While that of the inter-turn faults in phases A, B and C are 77.5572, 61.6389 and 62.5581 respectively. Based on the Euclidean distances of the faults, Df and normal current signals, three classification points were set: K1 = 0.60 x 102, K2 = 0.80 x 102 and K3 = 1.00 x 102. For K2 ≥ Df ≥ K1 inter-turn faults is identified and for K3 ≥ Df ≥ K2 phase to phase fault identified. This will improve the induction motors stator’s fault diagnosis. Keywords: induction motor, stator fault classification, data acquisition system, Discrete Wavelet Transform

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Bouakoura, Mohamed, Mohamed-Said Naït-Saïd, and Nasreddine Nait-Said. "Incipient Inter-Turn Short Circuit Fault Estimation Based on a Faulty Model Observer and ANN-Method for Induction Motor Drives." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 12, no. 4 (August 23, 2019): 374–83. http://dx.doi.org/10.2174/2352096511666180705113021.

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Background: According to statistics, short circuit faults are the second most frequent faults in induction motors. Thus, in this paper, we investigated inter turn short circuit faults in their early stage. Methods: A new equivalent model of the induction motor with turn to turn fault on one phase has been developed. This model has been used to establish two schemes to estimate the severity of the short circuit fault. In the first scheme, the faulty model is considered as an observer, where a correction of an error between the measured and the estimated currents is the kernel of the fault severity estimator. However, to develop the second method, the model was required only in the training process of an artificial neural network (ANN). Since stator faults have a signature on symmetrical components of phase currents, the magnitudes and angles of these components were used with the mean speed value as inputs of the ANN. A simulation on MATLAB of both techniques has been performed with various stator frequencies. Results: The suggested schemes prove a unique efficiency in the estimation of incipient turn to turn fault. Besides, the ANN based scheme is less complex which reduces its implementation cost. Conclusion: To monitor the stator of an induction motor, the choice of the appropriate algorithm should be done according to the system in which the motor will be installed. If the motor is directing connected to the grid or fed via an inverter with a variable DC bus voltage, the observer would be better, otherwise, the ANN algorithm is recommended.

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Al-Ameri, Salem Mgammal, Ahmed Allawy Alawady, Mohd Fairouz Mohd Yousof, Muhammad Saufi Kamarudin, Ali Ahmed Salem, Ahmed Abu-Siada, and Mohamed I. Mosaad. "Application of Frequency Response Analysis Method to Detect Short-Circuit Faults in Three-Phase Induction Motors." Applied Sciences 12, no. 4 (February 16, 2022): 2046. http://dx.doi.org/10.3390/app12042046.

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The industry has widely accepted Frequency Response Analysis (FRA) as a reliable method to detect power transformers mechanical deformations. While the FRA technique has been recommended in recent literature as a potential diagnostic method to detect internal faults within rotating machines, detailed feasibility studies have not been fully addressed yet. This paper investigates the feasibility of using the FRA technique to detect several short circuit faults in the stator winding of three-phase induction motors (TPIMs). In this regard, FRA testing is conducted on two sets of induction motors with various short circuit faults. Investigated faults include short circuits between two phases, short circuit turns within the same phase, phase-to-ground, and phase-to-neutral short circuit. The measured FRA signatures are divided into three frequency ranges: low, medium, and high. Several statistical indicators are employed to quantify the variation between faulty and healthy signatures in each frequency range. Experimental results attest the feasibility of the FRA technique as a diagnostic tool to detect internal faults in rotating machines, such as induction motors.

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Deeb, Muhammad, Gassan Ibragim, and Talal Assaf. "Diagnostics of Stator Winding Faults in a Three-Phase Asynchronous Motor Using Park’s Vector Analysis." Vestnik MEI, no. 5 (2021): 69–74. http://dx.doi.org/10.24160/1993-6982-2021-5-69-74.

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The study addresses the problem of detecting a short circuit fault in the three-phase induction motor winding by monitoring the stator current Park vector (Lissajous curves). Park's vector model is implemented using the Matlab software package. The experimental part of the study was carried out on an 11 kW three-phase induction motor. The Lissajous curves obtained for a healthy motor and a motor with short-circuited turns under various load conditions were compared with each other. The obtained results have demonstrated the effectiveness of the proposed method for detecting interturn short circuit faults in the three-phase stator windings of induction motors.

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Garcia-Guevara, Francisco M., Francisco J. Villalobos-Piña, Ricardo Alvarez-Salas, Eduardo Cabal-Yepez, and Mario A. Gonzalez-Garcia. "Stator Fault Detection in Induction Motors by Autoregressive Modeling." Mathematical Problems in Engineering 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/3409756.

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This study introduces a novel methodology for early detection of stator short circuit faults in induction motors by using autoregressive (AR) model. The proposed algorithm is based on instantaneous space phasor (ISP) module of stator currents, which are mapped toα-βstator-fixed reference frame; then, the module is obtained, and the coefficients of the AR model for such module are estimated and evaluated by order selection criterion, which is used as fault signature. For comparative purposes, a spectral analysis of the ISP module by Discrete Fourier Transform (DFT) is performed; a comparison of both methodologies is obtained. To demonstrate the suitability of the proposed methodology for detecting and quantifying incipient short circuit stator faults, an induction motor was altered to induce different-degree fault scenarios during experimentation.

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Rachmat Ah Ro Ufun, Bima, Iradiratu Diah Prahmana Karyatanti, and Belly Yan Dewantara. "Detection of Stator Winding Short Circuit Faults Through Magnetic Fields In Induction Motors." JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) 5, no. 1 (April 2, 2021): 89–102. http://dx.doi.org/10.21070/jeeeu.v5i1.1281.

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In applications in the industrial world, the use of induction motors has been widely used in operation because induction motors have many advantages, although they have many advantages, induction motors themselves also have disadvantages, namely having high starting currents. In many cases the damage to the induction motor, the damage to the stator due to a short circuit, is a frequent failure, this damage can cause considerable losses because the motor can stop operation So this research will discuss about the detection of short circuit faults in the stator winding through leaky flux using a flux sensor that is placed outside the motor and placed radially and using the Fast Fourier Transform (FFT) method. Damage to the short circuit is done by reconstructing the stator winding of the induction motor. There are two variations of short circuit damage, namely short circuit winding 1 to winding 3 and short circuit winding 2 to winding 10 on an induction motor. The short circuit data is then processed using the Fast Fourier Transform method which produces data in the form of voltage to frequency. The results of the percentage of success of short circuit fault detection seen from the loaders have an average percentage of 50%, at no load conditions can detect short circuit faults by 100%. In conditions of short circuit interruption 1-3 has a success percentage of 30% and short circuit fault 2-10 by 70%. The existence of this system is expected to be able to anticipate any damage that can cause considerable and fatal losses.

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HOCINE, Amina, Noureddine GAZZAM, and Atallah BENALIA. "Diagnosis of Fault in Doubly-Fed Three-Phase Induction Generator in Wind Power Applications." Electrotehnica, Electronica, Automatica 69, no. 1 (February 15, 2021): 44–50. http://dx.doi.org/10.46904/eea.21.69.1.1108006.

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Inter-turn short-circuits faults are one of the most common problems in Induction machines, especially in the Doubly-Fed Induction Generator Based-Wind Energy Conversion system. Accurate and fast fault diagnosis and detection minimize the down-time of the units as further damages can be prevented. In this paper, a robust fault detection approach for Doubly-Fed Induction Generator (DFIG) based on wind generators is proposed. The faults’ smart diagnosis and detection are based on the Sliding Mode Observer and Second Order Sliding Mode Control. The observer generates a residual for detection of stator Inter-Turn Short Circuit faults which can affect a system model. A decision system is used to process the residual vector to detect faults. In order to shed more light on the designed approach performances regarding the inter-turn short-circuit fault occurrence, simulations were implemented in MATLAB/Simulink environment.

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Mellah, H., S. Arslan, H. Sahraoui, K. E. Hemsas, and S. Kamel. "The Effect of Stator Inter-Turn Short-Circuit Fault on DFIG Performance Using FEM." Engineering, Technology & Applied Science Research 12, no. 3 (June 6, 2022): 8688–93. http://dx.doi.org/10.48084/etasr.4923.

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Doubly-Fed Induction Generators (DFIG) are operated for wind energy production, and as their capacity is increasing, their safety and reliability become more important. Several faults affect the performance of DFIG. The stator winding Inter-Turn Short-Circuit Fault (ITSCF) is one of the most prevalent electric machine failures. This study examined the stator ITSCF effects on DFIG performance for different cases. The DFIG was designed and engineered using the Finite Element Method (FEM) and the Maxwell software, and was examined in healthy operation and four defective cases with various Numbers of Inter-Turn Short Circuit Faults (NITSCF): 4, 9, 19, and 29. These models allowed the examination of the effects and the comparison of each case separately from the healthy state. The comparison was plotted in Matlab to show the effects of the faults. The novelty of this study was that it investigated the effects of different NITSCF on the performance of DFIG and not only on their effect on the stator current and distribution of magnetic flux density. A better understanding of the short circuit effects on the performance of the DFIG can be exploited for subsequent implementations of early fault detection systems.

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Aswad, Raya A. K., and Bassim M. H. Jassim. "Impact of Induction Motor Faults on the Basic Parameters' Values." Journal of Engineering 26, no. 12 (December 1, 2020): 66–80. http://dx.doi.org/10.31026/j.eng.2020.12.04.

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Unlike fault diagnosis approaches based on the direct analysis of current and voltage signals, this paper proposes a diagnosis of induction motor faults through monitoring the variations in motor's parameters when it is subjected to an open circuit or short circuit faults. These parameters include stator and rotor resistances, self-inductances, and mutual inductance. The genetic algorithm and the trust-region method are used for the estimation process. Simulation results confirm the efficiency of both the genetic algorithm and the trust-region method in estimating the motor parameters; however, better performance in terms of estimation time is obtained when the trust-region method is adopted. The results also show the possibility of extracting fault signatures from the motor's parameter values because each type of the mentioned faults has a different impact on these parameters. Under a 10% short circuit fault condition, the mutual inductance and rotor resistance deviate by almost 10% from their original values to lower values. While the stator resistance noticeably reduces by up to 20% during the open circuit fault condition.

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He, Rufei, Jian Qiao, Yumin Peng, Xianggen Yin, Yikai Wang, Hao Zhang, and Wenhui Wang. "A Rotor Winding Internal Short-Circuit Fault Protection Method for Variable-Speed Pumped Storage Units." Applied Sciences 12, no. 15 (August 2, 2022): 7783. http://dx.doi.org/10.3390/app12157783.

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In electrical machinery, the rotor windings’ internal short-circuit faults are addressed by the instantaneous over-current protection of the power electronic excitation device, which has low sensitivity and has difficulty meeting the safety requirements. In this paper, a rotor windings’ internal short-circuit fault protection method is proposed based on the harmonic characteristics of the circulating current between stator branches. The magnetomotive force distribution of the short-circuit coils in the rotor windings is theoretically deduced, and the characteristic frequencies of the circulating current between stator branches are analyzed. On this basis, the protection criterion of the rotor windings’ internal short-circuit fault is constructed by using the harmonic component of the circulating current. Then, an analytic model of the variable-speed pumped storage unit is established based on the multi-loop method, and the finite element method is used to verify the correctness of the proposed modeling method. An actual large variable-speed pumped storage unit is taken as an example, and the possible faults under different slip ratios are simulated. In the simulation results, the stator branch circulation has the obvious characteristic frequency harmonic components, which is consistent with the theoretical analysis. It verifies the effectiveness of the proposed protection method. Finally, it is analyzed and verified that the proposed protection has a strong maloperation prevention ability under other kinds of faults.

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Ding, Shuo, Xiao Heng Chang, and Qing Hui Wu. "Application of Probabilistic Neural Networks in Fault Diagnosis of Three-Phase Induction Motors." Applied Mechanics and Materials 433-435 (October 2013): 705–8. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.705.

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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.

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Reyes-Malanche, Josue A., Francisco J. Villalobos-Pina, Efraın Ramırez-Velasco, Eduardo Cabal-Yepez, Geovanni Hernandez-Gomez, and Misael Lopez-Ramirez. "Short-Circuit Fault Diagnosis on Induction Motors through Electric Current Phasor Analysis and Fuzzy Logic." Energies 16, no. 1 (January 3, 2023): 516. http://dx.doi.org/10.3390/en16010516.

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Online monitoring of induction motors has increased significantly in recent years because these devices are essential components of any industrial process. Incipient fault detection in induction motors avoids interruptions in manufacturing processes and facilitates maintenance tasks to reduce induction motor timeout. Therefore, the proposal of novel approaches to assist in the detection and classification of induction motor faults is in order. In this work, a reliable and noninvasive novel technique that does not require computational demanding operations, since it just performs arithmetic calculations, is introduced for detecting and locating short-circuit faults in the stator windings of an induction motor. This method relies on phasor analysis and the RMS values of line currents, followed by a small set of simple if-then rules to perform the diagnosis and identification of stator winding faults. Obtained results from different experimental tests on a rewound induction motor stator to induce short-circuit faults demonstrate that the proposed approach is capable of identifying and locating incipient and advanced deficiencies in the windings’ insulation with high effectiveness.

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Qiao, Jian, Yikai Wang, Rufei He, Wenhui Wang, Xianggen Yin, Yumin Peng, and Hao Zhang. "Rotor Winding Short-Circuit-Fault Protection Method for VSPSGM Combining the Stator and Rotor Currents." Applied Sciences 12, no. 18 (September 8, 2022): 9051. http://dx.doi.org/10.3390/app12189051.

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Rotor winding short circuit faults are common faults for variable-speed pumped-storage generator-motors (VSPSGM). At present, the exciting rotor fault protection of VSPSGM is simple and has low sensitivity. It can only act when the instantaneous value of the rotor phase current reaches three times the rated current. Therefore, it is difficult to cover some rotor winding short-circuit faults with weak fault characteristics. It is urgent to study a novel rotor winding short-circuit-fault protection method for VSPSGM. In this paper, a protection method that combines the stator and rotor currents with different frequencies is proposed. The characteristics of the stator and rotor currents before and after the fault is analyzed by using Clark transformation. On this basis, a specific protection criterion is constructed based on the discrete integral operation, which is easy to implement and not affected by the change of rotor speed. Then, the calculation method of the protection setting is proposed, considering the effect of unbalanced voltage and sensor measurement error. Simulation results show that the proposed method can reliably realize the protection of rotor winding faults. It has faster protection action speed than other methods in the same field. The protection coverage rate is over 90%.

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AFANAS’YEV, Alexander A. "Equations of a magnetoelectric valve motor for loop closures of the stator winding." Elektrichestvo 11, no. 11 (2020): 47–52. http://dx.doi.org/10.24160/0013-5380-2020-11-47-52.

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The article considers the differential equations of a switched permanent magnet motor in which a short-circuit fault occurred in one or more turns in one of parallel stator winding branches. Owing to the occurred asymmetry of the phase quantities, symmetrical line-to-line voltages at the stator winding terminals are assumed. It is shown that turn-to-turn short-circuit faults give rise to non-sinusoidal and imbalanced phase currents and voltages at the nominal load torque on the shaft, and it should be noted that initially, a growth of the frequency and ratios of currents in the phases with an increase in the number of short-circuited turns are observed, after which the phase currents tend to decrease (with a continuing growth in the current through the short-circuited loop), and the rotor stalling occurs. The growth of motor rotation frequency and decrease of its overloading capacity take place due to a growth in the demagnetizing effect of armature reaction caused by the current through the short-circuited stator winding turns.

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CHOUROUK, BOUCHAREB, NAIT-SAID MOHAMED-SAID, and LAHMER FETHI. "Modeling and Diagnostic of Permanent Magnet Synchronous Machine under Insulation Failure Condition." Algerian Journal of Signals and Systems 2, no. 2 (February 2, 2021): 86–95. http://dx.doi.org/10.51485/ajss.v2i2.35.

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One of the most frequent faults in PMSM stator is the turn-to-turn short-circuit fault. So, the aim of this paper is to present a dynamic model for PMSM with turn-to-turn short-circuit fault based on equivalent electric circuit model. Two simple and useful diagnostic techniques ESA an EPVA based on frequency analysis are applied to detect this kind of fault. The accuracy of diagnosis is based on adding the real waveform of back-EMF.

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Khadar, Saad, Abdellah Kouzou, Mohamed Mounir Rezzaoui, and Ahmed Hafaifa. "Sensorless Control Technique of Open-End Winding Five Phase Induction Motor under Partial Stator Winding Short-Circuit." Periodica Polytechnica Electrical Engineering and Computer Science 64, no. 1 (September 13, 2019): 2–19. http://dx.doi.org/10.3311/ppee.14306.

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Open-end winding induction machines are gaining more attention in the last years due to their attractive advantages in the industrial applications, where high reliability is required. However, despite their inherit robustness, they are subjected to various electrical or mechanical faults that can ultimately reduce the motor efficiency and later leads to full failure. This paper proposes a method of modeling the five phase induction machine with open end stator winding taking into consideration the short-circuit fault between turns. The fault modeling is based on the theory of electromagnetic coupling of electrical circuits. In addition, a sliding mode observer is used to estimate the speed rotor. The idea of proposed backstepping strategy is used in this paper to allow to the studied machine to continue its operating state under short circuit fault between turns. The proposed sensorless control strategy is evaluated in terms of the healthy and faulty performances through the simulation results presented in this paper. The obtained results prove that the proposed sensorless control technique allows to the open-end winding five phase induction machine to continue its operation mode under the specified fault of partial short-circuit of the stator winding. This can be a very practical situation in the industrial applications, especially in the case where the maintenance is not easy and the operation of the industrial process should not be interrupted suddenly.

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Xu, Xiaowei, Jingyi Feng, Hongxia Wang, Nan Zhang, and Xiaoqing Wang. "Dynamics Analysis of Misalignment and Stator Short-Circuit Coupling Fault in Electric Vehicle Range Extender." Processes 8, no. 9 (August 25, 2020): 1037. http://dx.doi.org/10.3390/pr8091037.

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Due to the complex structure and wide excitation of the range extender, the misalignment and stator short-circuit coupling fault can easily occur. Therefore, it is necessary to study the coupling fault mechanism of the range extender, analyze the cause of the fault and the fault evolution law, and research the coupling fault characteristics. To reveal the mechanism of misalignment and stator-short-circuit coupling fault, the misalignment mechanism was analyzed and the bending and torsion electromagnetic stiffness of the generator in the stator short-circuit fault was derived. Then the dynamic model of bending and torsion coupling for the generator was established. Furthermore, we used the Runge-Kutta method to study the vibration response characteristics of generator rotor under coupling fault. Then through finite element analysis, the feasibility of coupled fault diagnosis was verified. The results show that the response of the generator rotor not only has the frequency component of single faults, but also new frequency components such as 4.0 and 6.0 harmonic amplitudes of radial vibration and 3.0 harmonic amplitudes of torsional vibration, respectively.

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Ding, Shuo, Xiao Heng Chang, and Qing Hui Wu. "Fault Diagnosis of Induction Motors Based on RBF Neural Network." Applied Mechanics and Materials 462-463 (November 2013): 85–88. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.85.

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In order to improve the diagnosis accuracy of stator short circuit faults of three-phase induction motors, in this paper, a method using three-layered RBF neural network is proposed to diagnose the short circuit faults on the basis of analysis of structure and algorithm of RBF neural network. Then the approach to establish RBF neural network and the influence of different expanding coefficients upon the diagnosis accuracy are illustrated. The simulation results show that RBF neural network can successfully diagnose and classify six typical short circuit faults of induction motors. This method has a faster speed, higher accuracy and it needs fewer samples. In conclusion, RBF neural network is practical, efficient and intelligent in fault diagnosis of induction motors.

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Awachat, Mr Saurabh, Mr Piyush Raulkar, Mr Umesh Gakre, and Er Sandeep K. Mude. "Analysis and Simulation of Inter-Turn Fault Of Synchronous Generator Using MATLAB." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 593–97. http://dx.doi.org/10.22214/ijraset.2022.42174.

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Abstract: The paper represents a comprehensive analysis of the inter-turn faults of a synchronous generator. In today's power system, we have used three phase synchronous generator to generate electric power, thus we all always try to reduce the loss in order to improve the efficiency of the alternator. We basically focus more on the stator winding faults. Stator winding has unbalanced loading, field failure and stator winding fault (including line to ground fault, line to line fault and double line to ground fault, three phase fault and inter turn fault) Alternator stator inter turn faults are considered to be rare and therefore they are not taken into that serious consideration while designing the protection system for the alternator. But we knew that there could be an inter-turn short circuit fault in the stator winding of the alternator. Early detection of inter turn faults will eliminate damage to the stator core and adjacent coils, reducing repair costs and generator outage times. Since inter turn fault causes imbalance in phase voltage, this concept is discussed in this paperfor inter turn fault detection. The negative sequence voltage of the generator is used as a fault indicator for inter turn fault detection. This new approach is done using MATLAB software. Also, this method works for external as well as internal fault and helps in keeping the generator winding healthy. Keywords: Inter Turn fault, internal and external faults, Internal Negative, Sequence Generator Voltage.

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Pineda-Sanchez, Manuel, Ruben Puche-Panadero, Javier Martinez-Roman, Angel Sapena-Bano, Martin Riera-Guasp, and Juan Perez-Cruz. "Partial Inductance Model of Induction Machines for Fault Diagnosis." Sensors 18, no. 7 (July 18, 2018): 2340. http://dx.doi.org/10.3390/s18072340.

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The development of advanced fault diagnostic systems for induction machines through the stator current requires accurate and fast models that can simulate the machine under faulty conditions, both in steady-state and in transient regime. These models are far more complex than the models used for healthy machines, because one of the effect of the faults is to change the winding configurations (broken bar faults, rotor asymmetries, and inter-turn short circuits) or the magnetic circuit (eccentricity and bearing faults). This produces a change of the self and mutual phase inductances, which induces in the stator currents the characteristic fault harmonics used to detect and to quantify the fault. The development of a machine model that can reflect these changes is a challenging task, which is addressed in this work with a novel approach, based on the concept of partial inductances. Instead of developing the machine model based on the phases’ coils, it is developed using the partial inductance of a single conductor, obtained through the magnetic vector potential, and combining the partial inductances of all the conductors with a fast Fourier transform for obtaining the phases’ inductances. The proposed method is validated using a commercial induction motor with forced broken bars.

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Sang, Jun Yong, Chen Hao, and Peng Chao Wang. "Diagnosis of Stator Winding Inter-Turn Circuit Faults in Induction Motors Based on Wavelet Packet Analysis and Neural Network." Advanced Materials Research 529 (June 2012): 37–42. http://dx.doi.org/10.4028/www.scientific.net/amr.529.37.

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Aiming at the problem of the traditional stator current frequency spectrum analysis method cannot completely guarantee the accurate identification of stator winding inter-turn faults,the diagnosis method of stator winding inter-turn based on wavelet packet analysis (WPA) and Back Propagation (BP) neural network is put forward. The finite element model of the three-phase asynchronous motor which is based on Magnet is established, and analysis the magnetic flux density and current of the motor through simulation in normal and in the situation of short-circuit fault of stator winding inter-turn, the current signal of stator is analysised by wavelet packet , and the feature vector of frequency band energy is extracted as the basis to judge the state of induction motor running, and the motor state is identified by BP neural network, and the mapping from feature vector to the motor state is established. Simulation results show that: The diagnosis system of inter-turn fault based on WPA and BP neural network can effectively identify short-circuit fault between ratios. This is to say that the method has a high fault diagnosis rate.

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Wu, Yucai, and Guanhua Ma. "Anti-Interference and Location Performance for Turn-to-Turn Short Circuit Detection in Turbo-Generator Rotor Windings." Energies 12, no. 7 (April 10, 2019): 1378. http://dx.doi.org/10.3390/en12071378.

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Online and location detection of rotor winding inter-turn short circuits are an important direction in the field of fault diagnosis in turbo-generators. This area is facing many difficulties and challenges. This study is based on the principles associated with the U-shaped detection coil method. Compared with dynamic eccentricity faults, the characteristics of the variations in the main magnetic field after a turn-to-turn short circuit in rotor windings were analyzed and the unique characteristics were extracted. We propose that the degree of a turn-to-turn short circuit can be judged by the difference in the induction voltage of the double U-shaped detection coils mounted on the stator core. Here, the faulty slot position was determined by the local convex point formed by the difference in the induced voltage. Numerical simulation was used here to determine the induced voltage characteristics in the double U-shaped coils caused by the turn-to-turn short circuit fault. We analyzed the dynamic eccentricity fault as well as combined the fault of a turn-to-turn short circuit and dynamic eccentricity. Finally, we demonstrate the positive anti-interference performance associated with this fault detection method. This new online detection method is satisfactory in terms of sensitivity, speed, and positioning, and overall performance is superior to the traditional online detection methods.

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Tomczyk, Marcin, Ryszard Mielnik, Anna Plichta, Iwona Gołdasz, and Maciej Sułowicz. "Application of Genetic Algorithm for Inter-Turn Short Circuit Detection in Stator Winding of Induction Motor." Energies 14, no. 24 (December 17, 2021): 8523. http://dx.doi.org/10.3390/en14248523.

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This paper presents a new method of inter-turn short-circuit detection in cage induction motors. The method is based on experimental data recorded during load changes. Measured signals were analyzed using a genetic algorithm. This algorithm was next used in the diagnostics procedure. The correctness of fault detection was verified during experimental tests for various configurations of inter-turn short-circuits. The tests were run for several relevant diagnostic signals that contain symptoms of faults in an examined cage induction motor. The proposed algorithm of inter-turn short-circuit detection for various levels of winding damage and for various loads of the examined motor allows one to state the usefulness of this diagnostic method in normal industry conditions of motor exploitation.

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Bensaoucha, Saddam, Youcef Brik, Sandrine Moreau, Sid Ahmed Bessedik, and Aissa Ameur. "Induction machine stator short-circuit fault detection using support vector machine." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 40, no. 3 (May 21, 2021): 373–89. http://dx.doi.org/10.1108/compel-06-2020-0208.

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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.

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Pietrzak, Przemyslaw, and Marcin Wolkiewicz. "On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm." Electronics 10, no. 15 (July 26, 2021): 1786. http://dx.doi.org/10.3390/electronics10151786.

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The significant advantages of permanent magnet synchronous motors, such as very good dynamic properties, high efficiency and power density, have led to their frequent use in many drive systems today. However, like other types of electric motors, they are exposed to various types of faults, including stator winding faults. Stator winding faults are mainly inter-turn short circuits and are among the most common faults in electric motors. In this paper, the possibility of using the spectral analysis of symmetrical current components to extract fault symptoms and the machine-learning-based K-Nearest Neighbors (KNN) algorithm for the detection and classification of the PMSM stator winding fault is presented. The impact of the key parameters of this classifier on the effectiveness of stator winding fault detection and classification is presented and discussed in detail, which has not been researched in the literature so far. The proposed solution was verified experimentally using a 2.5 kW PMSM, the construction of which was specially prepared for carrying out controlled inter-turn short circuits.

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He, Yu-Ling, Wei-Qi Deng, Bo Peng, Meng-Qiang Ke, Gui-Ji Tang, Shu-Ting Wan, and Xiang-Yu Liu. "Stator Vibration Characteristic Identification of Turbogenerator among Single and Composite Faults Composed of Static Air-Gap Eccentricity and Rotor Interturn Short Circuit." Shock and Vibration 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/5971081.

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This paper investigates the radial stator vibration characteristics of turbogenerator under the static air-gap eccentricity (SAGE) fault, the rotor interturn short circuit (RISC) fault, and the composite faults (CFs) composed of SAGE and RISC, respectively. Firstly, the impact of the faulty types on the magnetic flux density (MFD) is analyzed, based on which the detailed expressions of the magnetic pull per unit area (MPPUA) on the stator under different performing conditions are deduced. Then, numerical FEM simulations based on Ansoft and an experimental study are carried out, taking the SDF-9 type fault simulating generator as the study object. It is shown that SAGE will increase the stator vibration at 2f(fis the electrical frequency) which already exists even in normal condition, while RISC and CF will bring in stator vibrations atf, 2f, 3f, and 4fat the same time. The vibration amplitudes under CF are larger than those under RISC. As SAGE increases, the vibration amplitudes of each harmonic component under CF will all be increased, while the development of RISC will decrease the 2nd harmonic vibration but meanwhile increase the 4th harmonic vibration. The achievements of this paper are beneficial for fault identification and condition monitoring of the turbogenerator.

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Skowron, Maciej, Marcin Wolkiewicz, Teresa Orlowska-Kowalska, and Czeslaw Kowalski. "Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors." Applied Sciences 9, no. 4 (February 13, 2019): 616. http://dx.doi.org/10.3390/app9040616.

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Electrical winding faults, namely stator short-circuits and rotor bar damage, in total constitute around 50% of all faults of induction motors (IMs) applied in variable speed drives (VSD). In particular, the short circuits of stator windings are recognized as one of the most difficult failures to detect because their detection makes sense only at the initial stage of the damage. Well-known symptoms of stator and rotor winding failures can be visible in the stator current spectra; however, the detection and classification of motor windings faults usually require the knowledge of human experts. Nowadays, artificial intelligence methods are also used in fault recognition. This paper presents the results of experimental research on the application of the stator current symptoms of the converter-fed induction motor drive to electrical fault detection and classification using Kohonen neural networks. The experimental tests of a diagnostic setup based on a virtual measurement and data pre-processing system, designed in LabView, are described. It has been shown that the developed neural detectors and classifiers based on self-organizing Kohonen maps, trained with the instantaneous symmetrical components of the stator current spectra (ISCA), enable automatic distinguishing between the stator and rotor winding faults for supplying various voltage frequencies and load torque values.

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Pietrzak, Przemyslaw, and Marcin Wolkiewicz. "Comparison of Selected Methods for the Stator Winding Condition Monitoring of a PMSM Using the Stator Phase Currents." Energies 14, no. 6 (March 15, 2021): 1630. http://dx.doi.org/10.3390/en14061630.

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Stator winding faults are one of the most common faults of permanent magnet synchronous motors (PMSMs), and searching for methods to efficiently detect this type of fault and at an early stage of damage is still an ongoing, important topic. This paper deals with the selected methods for detecting stator winding faults (short-circuits) of a permanent magnet synchronous motor, which are based on the analysis of the stator phase current signal. These methods were experimentally verified and their effectiveness was carefully compared. The article presents the results of experimental studies obtained from the spectral analysis of the stator phase current, stator phase current envelope, and the discrete wavelet transform. The original fault indicators (FIs) based on the observation of the symptoms of stator winding fault were distinguished using the aforementioned methods, which clearly show which symptom is most sensitive to the incipient fault of the stator winding of PMSMs.

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Eftekhari, Maryam, Mehdi Moallem, Saeed Sadri, and Min-Fu Hsieh. "Online Detection of Induction Motor's Stator Winding Short-Circuit Faults." IEEE Systems Journal 8, no. 4 (December 2014): 1272–82. http://dx.doi.org/10.1109/jsyst.2013.2288172.

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Amanuel, Thomas, Amanuel Ghirmay, Huruy Ghebremeskel, Robel Ghebrehiwet, and Weldekidan Bahlibi. "Design of Vibration Frequency Method with Fine-Tuned Factor for Fault Detection of Three Phase Induction Motor." Journal of Innovative Image Processing 3, no. 1 (April 12, 2021): 52–65. http://dx.doi.org/10.36548/jiip.2021.1.005.

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This research article focuses on industrial applications to demonstrate the characterization of current and vibration analysis to diagnose the induction motor drive problems. Generally, the induction motor faults are detected by monitoring the current and proposed fine-tuned vibration frequency method. The stator short circuit fault, broken rotor bar fault, air gap eccentricity, and bearing fault are the common faults that occur in an induction motor. The detection process of the proposed method is based on sidebands around the supply frequency in the stator current signal and vibration. Moreover, it is very challenging to diagnose the problem that occur due to the complex electromagnetic and mechanical characteristics of an induction motor with vibration measures. The design of an accurate model to measure vibration and stator current is analyzed in this research article. The proposed method is showing how efficiently the root cause of the problem can be diagnosed by using the combination of current and vibration monitoring method. The proposed model is developed for induction motor and its circuit environment in MATLAB is verified to perform an accurate detection and diagnosis of motor fault parameters. All stator faults are turned to turn fault; further, the rotor-broken bar and eccentricity are structured in each test. The output response (torque and stator current) is simulated by using a modified winding procedure (MWP) approach by tuning the winding geometrical parameter. The proposed model in MATLAB Simulink environment is highly symmetrical, which can easily detect the signal component in fault frequencies that occur due to a slight variation and improper motor installation. Finally, this research article compares the other existing methods with proposed method.

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Hussein, H. A. Taha, M. E. Ammar, and M. A. Moustafa Hassan. "Three Phase Induction Motor's Stator Turns Fault Analysis Based on Artificial Intelligence." International Journal of System Dynamics Applications 6, no. 3 (July 2017): 1–19. http://dx.doi.org/10.4018/ijsda.2017070101.

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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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Khechekhouche, Abderrahmane, Abderrrahim Allal, and Zied Driss. "Comparative study of advanced techniques for the diagnosis of induction motors." Heritage and Sustainable Development 3, no. 1 (April 8, 2021): 16–22. http://dx.doi.org/10.37868/hsd.v3i1.49.

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This work is a comparative study between the various advanced technologies of diagnosis of induction motors published recently and to make a classification of these diagnostic techniques according to their sensitivities from experimental results of stator short-circuit faults between stator turns. By using the logarithmic FFT spectrum, we can discover the best method to detect faults in their early stages so that we can predict their faults and anticipate breakdowns that can be dangerous for people or the economy.

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He, Yu-Ling, Meng-Qiang Ke, Fa-Lin Wang, Gui-Ji Tang, and Shu-Ting Wan. "EFFECT OF STATIC ECCENTRICITY AND STATOR INTER-TURN SHORT CIRCUIT COMPOSITE FAULT ON ROTOR VIBRATION CHARACTERISTICS OF GENERATOR." Transactions of the Canadian Society for Mechanical Engineering 39, no. 4 (December 2015): 767–81. http://dx.doi.org/10.1139/tcsme-2015-0061.

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This paper investigates the radial rotor vibration characteristics under static air-gap eccentricity and stator inter-turn short circuit composite faults. The air-gap magnetic flux density is firstly deduced to obtain the unbalanced magnetic pull (UMP) on rotor. Then the rotor vibration characters, as well as the developing trend between the faulty parameters and the vibration amplitudes, are analyzed. Finally, the experiments are taken on a SDF-9 type simulating generator. It is shown that the radial deformation possibility, the 2nd, 4th, and 6th harmonic vibrations will be caused by the composite faults. Besides, the development of the inter-turn short circuit, the increment of the static eccentricity, and the rise of the exciting current will all get the deformation trend and the vibration amplitudes increased.

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Dybkowski, Mateusz, and Szymon Antoni Bednarz. "Modified Rotor Flux Estimators for Stator-Fault-Tolerant Vector Controlled Induction Motor Drives." Energies 12, no. 17 (August 22, 2019): 3232. http://dx.doi.org/10.3390/en12173232.

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This paper deals with fault-tolerant control (FTC) of an induction motor (IM) drive. An inter-turn short circuit (ITSC) of the stator windings was taken into consideration, which is one of the most common internal faults of induction machines. The sensitivity of the classic, well-known voltage and current models to the stator winding faults was analyzed. It has been shown that these classical state variable estimators are sensitive to induction motor parameter changes during stator winding failure, which results in unstable operation of the direct field-oriented control (DFOC) drive. From a safety-critical applications point of view, it is vital to guarantee stable operation of the drive even during faults of the machine. Therefore, a new FTC system has been proposed, which consists of new modified rotor flux estimators, robust to stator winding faults. A detailed description of the proposed system is presented herein, as well as the results of simulation and experimental tests. Simulation analyses were performed using MATLAB/Simulink software. Experimental tests were carried out on the experimental test bench with a dSpace DS1103 card. The proposed solution could be applied as an alternative rotor flux estimation technique for the modern FTC drive.

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Zimnickas, Tomas, Jonas Vanagas, Karolis Dambrauskas, Artūras Kalvaitis, and Mindaugas Ažubalis. "Application of Advanced Vibration Monitoring Systems and Long Short-Term Memory Networks for Brushless DC Motor Stator Fault Monitoring and Classification." Energies 13, no. 4 (February 13, 2020): 820. http://dx.doi.org/10.3390/en13040820.

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In this research, electric motors faults and their identification is reviewed. Brushless direct-current (BLDC) motors stator fault identification using long short-term memory neural networks were analyzed. A proposed method of vibration data acquisition using cloud technologies with high accuracy, feature extraction using spectral entropy, and instantaneous frequency and standardization using mean and standard deviation was reviewed. Additionally, model training with raw and standardized data was compared. A total model accuracy of 97.10 percent was achieved. The proposed methods could successfully identify the motor stator status from normal, to loss of stator winding imminent and arcing, and lastly to open circuit in stator winding—motor needing to stop immediately—by using gathered data from real experiments, training the model and testing it theoretically.

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Pusca, Remus, Raphael Romary, Ezzeddine Touti, Petru Livinti, Ilie Nuca, and Adrian Ceban. "Procedure for Detection of Stator Inter-Turn Short Circuit in AC Machines Measuring the External Magnetic Field." Energies 14, no. 4 (February 20, 2021): 1132. http://dx.doi.org/10.3390/en14041132.

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This paper presents a non-invasive procedure to detect inter-turn short circuit faults in the stator windings of AC electrical machines. It proposes the use of the stray external magnetic field measured in the vicinity of the machine to determine stator faults. The originality introduced by this procedure is the analysis method presented in the paper, which when compared to usual diagnosis methods, does not require any data on the healthy state of the machine. The procedure uses the magnetic unbalance created by the rotor poles and the load variation in faulty cases. The presented method can be applied to induction and synchronous machines used as a motor or generator. It is based on the variation of sensitive spectral lines obtained from the external magnetic field when the load changes. Analytical relationships are developed in the paper to justify the proposed method and to explain the physical phenomenon. To illustrate these theoretical considerations, practical experiments are also presented.

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Vicente, João, Ângela Ferreira, Marcelo Castoldi, João Teixeira, and Alessandro Goedtel. "Stator Winding Fault Detection Using External Search Coil and Artificial Neural Network." MATEC Web of Conferences 322 (2020): 01054. http://dx.doi.org/10.1051/matecconf/202032201054.

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This paper presents a methodology for winding stator fault detection of induction motors, using an external search coil, which is a noninvasive technique and can be applied during motor operation. The dispersion magnetic flux of the motor operating in abnormal conditions induces a voltage in the search coil that differs from a reference pattern corresponding to the healthy stator winding. Experimental data were obtained in a test bench using a 0.75 kW three-phase squirrel-cage induction motor with the stator winding modified to allow the introduction of short circuits. This work considered short circuits in one phase, involving 1%, 3%, 5% and 10% of the turns, with the motor loaded with a varying torque. Fault diagnosis is obtained through two models of artificial neural networks, implemented with the signals in the time domain. The obtained results demonstrated that the developed methodology presents difficulties in predicting short circuits in incipient stages, but for short circuits of higher severity, the behaviour improved substantially, being 100% successful for faults with 10% turns short-circuited.

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Toumi, Djilali, Mohamed Boucherit, and Mohamed Tadjine. "Observer-based fault diagnosis and field oriented fault tolerant control of induction motor with stator inter-turn fault." Archives of Electrical Engineering 61, no. 2 (June 1, 2012): 165–88. http://dx.doi.org/10.2478/v10171-012-0015-1.

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Observer-based fault diagnosis and field oriented fault tolerant control of induction motor with stator inter-turn fault This paper describes a fault-tolerant controller (FTC) of induction motor (IM) with inter-turn short circuit in stator phase winding. The fault-tolerant controller is based on the indirect rotor field oriented control (IRFOC) and an observer to estimate the motor states, the amount of turns involved in short circuit and the current in the short circuit. The proposed fault controller switches between the control of the two components of measured stator current in the synchronously rotating reference frame and the control of the two components of estimated current in the case of faulty condition when the estimated current in the short circuit is not destructive of motor winding. This technique is used to eliminate the speed and the rotor flux harmonics and to assure the decoupling between the rotor flux and torque controls. The results of the simulation for controlling the speed and rotor flux of the IM demonstrate the applicability of the proposed FTC.

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Gao, Cai Xia, Chen Hao, and Yue Bing Zhao. "Detection and Analysis of Stator Winding Inter-Turn Short Circuit Fault in Permanent Magnet Linear Synchronous Motor." Advanced Materials Research 529 (June 2012): 322–26. http://dx.doi.org/10.4028/www.scientific.net/amr.529.322.

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A two-dimensional finite element model of PMLSM is build based on the finite element analysis software Magnet to research the diagnosis of stator winding inter-turn short circuit fault in PMLSM. The velocity, thrust, the stator current performance curve are obtained by simulation using Magnet when PMLSM is normal and under different extent inter-turn short circuit fault, the harmonic content of speed and thrust are analyzed using Matlab / Simulink , the conclusion that the thrust of the harmonic content is used as the Permanent Magnet Linear Synchronous Motor (PMLSM) stator inter-turn short circuit fault feature is proposed , which provided a basis for detection of stator winding inter-turn short circuit fault in PMLSM.

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Xu, Bo Qiang, and Jing Ting Wang. "Fault Analysis of Stator Inter-Turn Short Circuit in Doubly Fed Induction Machines." Advanced Materials Research 912-914 (April 2014): 679–83. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.679.

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The doubly fed induction generator (DFIG) is an important component of wind turbine systems,so the fault analysis is great necessary.This paper shows a way to analysis the features of stator inter-short circuit fault,which is based on multi-circuit theory.The fault features of stator inter-turn short circuit in doubly fed induction are analyzed in this paper,deeply and thoroughly.Then a multi-loop model was carried out to simulate 5.5kW DFIG ,which was in normal conditions and in cases of fault of stator inter-turn short circuit,respectively.A fourier transformation was applied to analysis the simulation results,especially about short circuit current.

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An, Guoqing, and Hongru Li. "Stator and Rotor Faults Diagnosis of Squirrel Cage Motor Based on Fundamental Component Extraction Method." International Journal of Rotating Machinery 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/1576381.

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Nowadays, stator current analysis used for detecting the incipient fault in squirrel cage motor has received much attention. However, in the case of interturn short circuit in stator, the traditional symmetrical component method has lost the precondition due to the harmonics and noise; the negative sequence component (NSC) is hard to be obtained accurately. For broken rotor bars, the new added fault feature blanked by fundamental component is also difficult to be discriminated in the current spectrum. To solve the above problems, a fundamental component extraction (FCE) method is proposed in this paper. On one hand, via the antisynchronous speed coordinate (ASC) transformation, NSC of extracted signals is transformed into the DC value. The amplitude of synthetic vector of NSC is used to evaluate the severity of stator fault. On the other hand, the extracted fundamental component can be filtered out to make the rotor fault feature emerge from the stator current spectrum. Experiment results indicate that this method is feasible and effective in both interturn short circuit and broken rotor bars fault diagnosis. Furthermore, only stator currents and voltage frequency are needed to be recorded, and this method is easy to implement.

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Cheng, Siwei, and Thomas Habetler. "Using Only the DC Current Information to Detect Stator Turn Faults in Automotive Claw-Pole Generators." Industrial Electronics, IEEE Transactions on 60, no. 8 (April 2013): 3462–71. http://dx.doi.org/10.1109/tie.2012.2205353.

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Abstract:

The stator turn-to-turn short circuit is an important type of fault in automotive claw-pole generators. In a typical vehicle electric power system, the built-in rectifier of the generator makes it difficult to access the ac current or voltage information, rendering conventional sequence-component-based fault-detection methods useless. To detect such fault using only the available sensor information, a dynamic model of the claw-pole generator with stator turn faults is derived in this paper to analyze how the fault would interact with the connected battery and the static full-bridge rectifier and how it would affect the generator's output voltage and current. It is found that, in the rectified generator output current, the harmonic at one-third of the rectifier ripple frequency is a robust signature of the stator turn fault. The performance of the stator turn-fault detector is demonstrated by extensive experimental results. Although the fault detector is originally proposed for claw-pole generators, it is also applicable to most polyphase ac generators with a dc-link rectifier.

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Journal articles on the topic 'Stator short circuit faults'

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