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Journal articles on the topic 'Stator winding turn fault'
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1
Zamarrón, A., and M. A. Arjona. "Detection of Stator-Winding Turn-to-Turn Faults in Induction Motors, Based on Virtual Instrumentation." International Journal of Electrical Engineering & Education 47, no. 1 (2010): 63–72. http://dx.doi.org/10.7227/ijeee.47.1.6.
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A virtual instrumentation system for stator turn-to-turn winding-fault detection is presented in this paper. The system can be adopted for industry and academy as a useful tool to prevent unexpected downtime or severe damage to induction motors. The high-frequency carrier-signal injection technique is used to obtain information on machine stator winding. Measurements of the resulting negative-sequence current are used to detect turn-to-turn faults. Since the stator windings are fixed in space, a turn-to-turn fault gives rise to a stationary saliency which appears as a DC component in the spectrum of the negative-sequence carrier-signal current. The proposed virtual instrument shows the relationship of the magnitude of the DC component to the grade of failure of the stator winding.
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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 (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.
3
Khadar, Saad. "Influence of a different fault scenarios on the properties of multi-phase induction machine." Algerian Journal of Engineering and Technology 02 (February 6, 2020): 0َ11–021. https://doi.org/10.5281/zenodo.3923074.
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This paper deals with the influence of a stator fault, power switch faults and open phase fault conditions on the properties of a five-phase induction machine under open-end stator winding (OeW-FPIM). This paper will develop an accurate mathematical model to simulate the faulty OeW-FPIM drives. The proposed model is based on the theory of electromagnetic coupling of electrical circuits coupled to the differential equation system governing the machine behavior in presence of the stator winding faults. In fact, when a short circuits between coils occurs, the stator winding function of the injured phase changes. As a consequence, the stator resistance, the stator inductance of this phase and its mutual inductance with all the other circuits change also. Consequently, the inductances and resistance matrices will be changed by taking into account the introduced coefficients of short-circuited turns. The performance of the OeW-FPIM drives have been tested via simulation under different fault scenarios conditions.
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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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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 (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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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 (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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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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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 (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.
10
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 (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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N., F. Fadzail, Mat Zali S., A. Khairudin M., and H. Hanafi N. "Stator winding fault detection of induction generator based wind turbine using ANN." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 19, no. 1 (2020): 126–33. https://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.
12
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 (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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Dybkowski, Mateusz, and Szymon Antoni Bednarz. "Modified Rotor Flux Estimators for Stator-Fault-Tolerant Vector Controlled Induction Motor Drives." Energies 12, no. 17 (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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Maraaba, Luqman S., Ssennoga Twaha, Azhar Memon, and Zakariya Al-Hamouz. "Recognition of Stator Winding Inter-Turn Fault in Interior-Mount LSPMSM Using Acoustic Signals." Symmetry 12, no. 8 (2020): 1370. http://dx.doi.org/10.3390/sym12081370.
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This paper presents a novel stator inter-turn fault diagnosis method for Line Start Permanent Magnet Synchronous Motors (LSPMSMs) using the frequency analysis of acoustic signals resulting from asymmetrical faults. In this method, acoustic data are experimentally collected from a 1 hp interior mount LSPMSM for different inter-turn fault cases and motor loading levels, while including the background noise. The signals are collected using a smartphone at a sampling rate of 48,000 samples per second. The signal for each case is analyzed using fast Fourier transform (FFT), which results in the decomposition of the signal into its frequency components. The results indicate that, for both no-load and full-load conditions, 39 components are observed to be affected by the faults, whereby their amplitudes increase with the fault severity. The 40-turns fault shows the highest difference in the component amplitudes compared with the healthy condition acoustic signal. Therefore, this diagnostic method is able to detect the stator inter-turn fault for interior mount LSPMSMs. Moreover, the method is simple and cheap since it uses a readily available sensor.
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Mishra, Partha, Shubhasish Sarkar, Sandip Saha Chowdhury, and Santanu Das. "Machine learning based stator-winding fault severity detection in induction motors." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 1 (2025): 182. https://doi.org/10.11591/ijeecs.v38.i1.pp182-192.
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Approximately 35% of all induction motor defects are caused by stator inter-turn faults. In this paper a novel algorithm has been proposed to analyze the three-phase stator current signals captured from the motor while it is in operation. The suggested method seeks to identify stator inter-turn short circuit faults in early stage and take the appropriate action to prevent the motor's condition from getting worse. Three-phase current signals have been captured under healthy and faulty conditions of the motor. Involving discrete wavelet transform (DWT) based decomposition followed by reconstruction using inverse DWT (IDWT), 50 Hz fundamental component has been removed from the captured raw current signals. Subsequently, from each phase current 15 statistical parameters have been retrieved. The statistical parameters include mean, standard deviation, skewness, kurtosis, peak-to-peak, root mean square (RMS), energy, crest factor, form factor, impulse factor, and margin factor. At the end, a standard machine learning algorithm namely error correcting output codes-support vector machine (ECOC-SVM) has been employed to classify six different severity of stator winding faults. The proposed fault diagnosis method is load and motor-rating independent.
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Partha, Mishra Shubhasish Sarkar Sandip Saha Chowdhury Santanu Das. "Machine learning based stator-winding fault severity detection in induction motors." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 1 (2025): 182–92. https://doi.org/10.11591/ijeecs.v38.i1.pp182-192.
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Approximately 35% of all induction motor defects are caused by stator inter-turn faults. In this paper a novel algorithm has been proposed to analyze the three-phase stator current signals captured from the motor while it is in operation. The suggested method seeks to identify stator inter-turn short circuit faults in early stage and take the appropriate action to prevent the motor's condition from getting worse. Three-phase current signals have been captured under healthy and faulty conditions of the motor. Involving discrete wavelet transform (DWT) based decomposition followed by reconstruction using inverse DWT (IDWT), 50 Hz fundamental component has been removed from the captured raw current signals. Subsequently, from each phase current 15 statistical parameters have been retrieved. The statistical parameters include mean, standard deviation, skewness, kurtosis, peak-to-peak, root mean square (RMS), energy, crest factor, form factor, impulse factor, and margin factor. At the end, a standard machine learning algorithm namely error correcting output codes-support vector machine (ECOC-SVM) has been employed to classify six different severity of stator winding faults. The proposed fault diagnosis method is load and motor-rating independent.
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Wei, Yang, Ligui Wu, Guangyao Li, et al. "Study on the Characteristics of Turn-to-Turn Short Circuit Faults in the Primary Windings of the Generator Terminal Voltage Transformer." Electronics 13, no. 23 (2024): 4772. https://doi.org/10.3390/electronics13234772.
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Turn-to-turn short circuit faults in the primary winding of generator terminal voltage transformers can lead to erroneous operation of stator grounding protection systems. This paper analyzes the fault characteristics associated with such failures and derives formulas for the fault phase current and zero-sequence voltage during a turn-to-turn short circuit in the primary winding. A 3D finite element model of the generator terminal voltage transformer is established by using Altair Flux 3D, and the accuracy of the model is verified. Based on this model, simulation tests were conducted to investigate turn-to-turn short circuits in the primary winding. The results reveal that as the number of shorted turns increases, the voltage of the fault phase decreases continuously while the voltages of the other two phases increase. The current in the short-circuited phase rises significantly, accompanied by an increase in zero-sequence voltage. Visualizations of magnetic field parameters indicate that as the number of shorted turns increases, the magnetic induction magnitude of the fault phase rises steadily and approaches saturation, resulting in heightened magnetic field intensity near the shorted turns. This analysis of fault characteristics through simulation contributes to the advancement of fault diagnosis systems for generator terminal voltage transformers.
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M. Ismail Gani. "Winding Fault Detection in Motor Using Near Infra-Red Sensor Signal Based Dywt and Tdywt Analysis." Journal of Information Systems Engineering and Management 10, no. 53s (2025): 284–94. https://doi.org/10.52783/jisem.v10i53s.10872.
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Introduction: Runtime monitoring of the stator and rotor windings of asynchronous motors is essential for early detection of faults. Early fault detection enables rapid recovery and prevents severe operational failures during critical stages. Rotor winding faults can lead to turn-to-turn faults, which in turn increase fault current, thermal stress, and the temperature of the motor windings. If inter-turn insulation faults are not identified promptly, they can escalate to rotor bending and coil burning. However, continuous surveillance of motor windings and insulation during operation is challenging. Objectives: This study aims to develop a more effective fault detection mechanism for asynchronous motors by addressing the limitations of traditional monitoring methods. The goal is to enhance the accuracy and reliability of fault diagnosis, particularly for rotor winding faults and inter-turn insulation failures, during motor operation. Methods: The proposed approach utilizes Near-Infrared (NIR) sensor-based analysis in conjunction with signal processing through two different wavelet transform techniques: dyadic and transverse dyadic wavelet transforms. These methods are used to analyse signals collected from the motor in real-time. Results: The analysis demonstrated a notable improvement in fault detection accuracy compared to traditional diagnostic methods. The use of NIR sensors with dyadic and transverse dyadic wavelet transforms enhanced the sensitivity and effectiveness of runtime monitoring, making it more reliable for early fault identification in asynchronous motor windings. Conclusions: Mi tempus imperdiet nulla malesuada. Magna fermentum iaculis eu non diam phasellus vestibulum. Consectetur adipiscing elit duis tristique sollicitudin nibh sit amet commodo. Elit scelerisque mauris pellentesque pulvinar. Et malesuada fames ac turpis egestas maecenas pharetra convallis posuere. Elementum integer enim neque volutpat ac tincidunt vitae semper.
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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 (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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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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Bouslimani, Samir, Samir Meradi, Said Drid, Larbi Chrifi-Alaoui, and Ali Bezziane. "State Estimation MRAS and Identification of Stator Winding Phase Fault Detection of the PMSG in Wind Energy Based on the Sliding Mode Control." Power Electronics and Drives 8, no. 1 (2023): 109–27. http://dx.doi.org/10.2478/pead-2023-0009.
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Abstract This paper proposes a method for the diagnosis of stator inter-turn short-circuit fault for permanent magnet synchronous generators (PMSG). Inter-turn short-circuit currents are among the most critical in PMSG. For safety considerations, a fast detection is required when a fault occurs. This approach uses the parameter estimation of the per-phase stator resistance in closed-loop control of variable speed of wind energy conversion system (WECS). In the presence of an incipient short-circuit fault, the estimation of the resistance of the stator in the d-q reference frame does not make it possible to give the exact information. To solve this problem, a novel fault diagnosis scheme is proposed using parameter estimation of the per-phase stator resistance. The per-phase stator resistance of PMSG is estimated using the MRAS algorithm technique in real time. Based on a faulty PMSG model expressed in Park’s reference frame, the number of short-circuited turns is estimated using MRAS. Fault diagnosis is on line detected by analysing the estimated stator resistance of each phase according to the fault condition. The proposed fault diagnosis scheme is implemented without any extra devices. Moreover, the information on the estimated parameters can be used to improve the control performance. The simulation results demonstrate that the proposed method can estimate the faulty phase.
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Belkhadir, Ahmed, Remus Pusca, Driss Belkhayat, Raphaël Romary, and Youssef Zidani. "Analytical Modeling, Analysis and Diagnosis of External Rotor PMSM with Stator Winding Unbalance Fault." Energies 16, no. 7 (2023): 3198. http://dx.doi.org/10.3390/en16073198.
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Multiple factors and consequences may lead to a stator winding fault in an external rotor permanent magnet synchronous motor that can unleash a complete system shutdown and impair performance and motor reliability. This type of fault causes disturbances in operation if it is not recognized and detected in time, since it might lead to catastrophic consequences. In particular, an external rotor permanent magnet synchronous motor has disadvantages in terms of fault tolerance. Consequently, the distribution of the air-gap flux density will no longer be uniform, producing fault harmonics. However, a crucial step of diagnosis and controlling the system condition is to develop an accurate model of the machine with a lack of turns in the stator winding. This paper presents an analytical model of the stator winding unbalance fault represented by lack of turns. Here, mathematical approaches are used by introducing a stator winding parameter for the analytical modeling of the faulty machine. This model can be employed to determine the various quantities of the machine under different fault levels, including the magnetomotive force, the flux density in the air-gap, the flux generated by the stator winding, the stator inductances, and the electromagnetic torque. On this basis, a corresponding link between the fault level and its signature is established. The feasibility and efficiency of the analytical approach are validated by finite element analysis and experimental implementation.
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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 (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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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 (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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Wang, Xu Hong, and Yi Gang He. "Detection for Stator Winding Turn Fault by Using Fuzzy Neural Network." Applied Mechanics and Materials 48-49 (February 2011): 415–18. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.415.
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A fuzzy neural network based on-line turn fault detection approach for induction motors is proposed in this paper. B-spline membership fuzzy neural network is employed to detect turn fault and a genetic algorithm with an efficient search strategy is developed to optimize network parameters. Based on it, Experiments are carried out on a special rewound laboratory induction motor, the results show fuzzy neural network based diagnosis model determines the shorted turns exactly, and is more effective than the parameters estimation method under the condition of detecting a slowly developing turn fault.
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Rocha, Rodolfo V., and Renato M. Monaro. "Algorithm for Fast Detection of Stator Turn Faultsin Variable-Speed Synchronous Generators." Energies 16, no. 5 (2023): 2491. http://dx.doi.org/10.3390/en16052491.
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Faults between stator winding turns of synchronous generators have led to specific changes in the harmonic content of currents. In this paper, these changes are used to detect faults in variable-speed synchronous generators connected to three-level converters during their operation. Currents typically measured for control purposes are used to avoid installation of additional sensors. The neutral point current of the three-level converter is also evaluated under faults in the generator. Encoder-tuned dynamic filters based on Park transformation and Fourier coefficients together with low-pass filters are used to detect the selected harmonics under variable speeds. The geometric loci of these components are proposed as a method to distinguish between healthy and faulty conditions. Simulation and experimental data are used to test sensitivity, selectivity and detection time of the proposed technique for different fault types. Generalization for a different generator is also presented and tested. Most fault cases were detected using the harmonics.
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Bai, Jie, Xuan Liu, Bingjie Dou, et al. "A Fault Diagnosis Method for Pumped Storage Unit Stator Based on Improved STFT-SVDD Hybrid Algorithm." Processes 12, no. 10 (2024): 2126. http://dx.doi.org/10.3390/pr12102126.
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Stator faults are one of the common issues in pumped storage generators, significantly impacting their performance and safety. To ensure the safe and stable operation of pumped storage generators, a stator fault diagnosis method based on an improved short-time Fourier transform (STFT)-support vector data description (SVDD) hybrid algorithm is proposed. This method establishes a fault model for inter-turn short circuits in the stator windings of pumped storage generators and analyzes the electrical and magnetic states associated with such faults. Based on the three-phase current signals observed during an inter-turn short circuit fault in the stator windings, the three-phase currents are first converted into two-phase currents using the principle of equal magnetic potential. Then, the STFT is applied to transform the time-domain signals of the stator’s two-phase currents into frequency-domain signals, and the resulting fault current spectrum is input into the improved SVDD network for processing. This ultimately outputs the diagnosis result for inter-turn short circuit faults in the stator windings of the pumped storage generator. Experimental results demonstrate that this method can effectively distinguish between normal and faulty states in pumped storage generators, enabling the diagnosis of inter-turn short circuit faults in stator windings with low cross-entropy loss. Through analysis, under small data sample conditions, the accuracy of the proposed method in this paper can be improved by up to 7.2%. In the presence of strong noise interference, the fault diagnosis accuracy of the proposed method remains above 90%, and compared to conventional methods, the fault diagnosis accuracy can be improved by up to 6.9%. This demonstrates that the proposed method possesses excellent noise robustness and small sample learning ability, making it effective in complex, dynamic, and noisy environments.
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Skowron, Maciej, Teresa Orlowska-Kowalska, Marcin Wolkiewicz, and Czeslaw T. Kowalski. "Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor." Energies 13, no. 6 (2020): 1475. http://dx.doi.org/10.3390/en13061475.
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In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using the diagnostic signal direct processing, which could replace well known analytical methods. Tests were carried out for various levels of stator failures. In order to assess the sensitivity of the applied CNN-based detector to motor operating conditions, the tests were carried out for variable load torques and for different values of supply voltage frequency. Experimental tests were conducted on a specially designed setup with the 3 kW induction motor of special construction, which allowed for the physical modelling of inter-turn short-circuits in each of the three phases of the machine. The on-line tests prove the possibility of using CNN in the real-time diagnostic system with the high accuracy of incipient stator winding fault detection and classification. The impact of the developed CNN structure and training method parameters on the fault diagnosis accuracy has also been tested.
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Hu, Hongqian, Weifeng Shi, and Benjamin Venditti. "Research on modeling and fault diagnosis of inter-turn short fault of permanent magnet synchronous motor." Journal of Computational Methods in Sciences and Engineering 20, no. 3 (2020): 959–73. http://dx.doi.org/10.3233/jcm-194127.
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The stator winding inter-turn short fault of permanent magnet synchronous motor (PMSM) is a common motor fault with high probability. If this fault is not detected and handled in time, small initial fault will rapidly develop into other faults such as grounding short, inter-phase short, winding short et al., and even the motor damage. In order to diagnose the inter-turn short fault of PMSM, this paper firstly builds the model of the inter-turn short fault of PMSM, and then proposes a fast and simple fault diagnosis method for extracting the features of the inter-turn short fault under noise. For the first time, by introducing the full-short-circuit resistance Rf that relates with the fault severity factor δ and by estimating the inductance of inter-turn short fault motor on-line according to the three principles of the inductance calculation, which is used in the inter-turn short fault of transformer winding, the inter-turn short analysis model of PMSM is deduced. The model can be used to simulate the performance not only the health of PMSM but also the fault of inter-turn short fault of PMSM in one phase winding. This paper analyses the transient and steady state performance of 3-phase unbalanced current, speed and electromagnetic torque ripple and the relationship between full-short-circuit resistance and short current under fault condition. Finally, a fast and simple on-line fault diagnosis method of PMSM with inter-turn short fault is proposed based on phase sensitive detection (PSD) algorithm. The method uses the fundamental and third harmonic features of inter-turn short fault current. The simulation results show that the model can be well suited for the simulation of PMSM with inter-turn short fault, and the method can effectively extract the features and diagnose the fault.
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Chen, Yujie, Leiting Zhao, Liran Li, Kan Liu, and Cunxin Ye. "Digital Twin-Based Online Diagnosis Method for Inter-Turn Short Circuit Fault in Stator Windings of Induction Motors." Energies 18, no. 12 (2025): 3063. https://doi.org/10.3390/en18123063.
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Inter-turn short-circuit fault is a common electrical issue in high-speed train traction motors, which can severely degrade motor performance and significantly shorten operational lifespan. Early detection is crucial for ensuring the safety of traction systems. This paper presents a digital twin-based method for diagnosing stator winding inter-turn short-circuit faults in induction motors. First, an advanced rapid-solving algorithm is employed to establish a real-time digital twin model of the motor under healthy conditions. Second, a mathematical model characterizing stator winding faults is developed. Subsequently, fault detection and localization are achieved through analyzing three-phase current residuals between the digital twin model and the actual system. Extensive simulations and experiments demonstrate that the proposed method generates a fault index amplitude approximately 20 times larger than traditional sampling-value-based prediction methods, indicating exceptional sensitivity. The approach is minimally invasive, requiring no additional measurement equipment. Moreover, it maintains diagnostic capability even under motor parameter mismatch conditions, outperforming traditional methods. The proposed method demonstrates distinct advantages for high-speed train traction systems. It enables real-time monitoring and predictive maintenance, effectively reducing operational costs while preventing catastrophic failures.
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Abitha Memala, W., and V. Rajini. "Wavelet Based Induction Motor Fault Diagnosis Using Zero Sequence Current." Journal of Computational and Theoretical Nanoscience 14, no. 1 (2017): 411–20. http://dx.doi.org/10.1166/jctn.2017.6336.
Full textAbstract:
Induction motor stator fault is diagnosed by applying Discrete Wavelet transform on zero sequence components. The single phasing stator fault is created and diagnosed in the induction motor model developed in stationary reference frame, under varying load conditions. The stator inter-turn incipient fault is created and diagnosed in the induction motor experimental setup as well under no load condition. The qdo components are calculated from Park’s equations. The faults can be diagnosed from wavelet transform of the zero sequence current components. PSD is used for diagnosing the fault and the statistical value is used for verifying the result. The energy is calculated using Parseval’s theorem. The energy and the statistical data calculated from the wavelet coefficients of zero sequence current components are used as fault indicators. The energy value is able to reveal the fault severity in the induction motor stator winding. Power spectral Density along with Discrete Wavelet Transform plays very important role in diagnosing the fault.
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Khadidja, El Merraoui, Ferdjouni Abdellaziz, and Bounekhla M'hamed. "Real time observer-based stator fault diagnosis for IM." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (2020): 210–22. https://doi.org/10.11591/ijece.v10i1.pp210-222.
Full textAbstract:
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.
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Ma, Jie, Yingxue Li, Liying Wang, et al. "Stator ITSC Fault Diagnosis for EMU Induction Traction Motor Based on Goertzel Algorithm and Random Forest." Energies 16, no. 13 (2023): 4949. http://dx.doi.org/10.3390/en16134949.
Full textAbstract:
The stator winding insulation system is the most critical and weakest part of the EMU’s (electric multiple unit’s) traction motor. The effective diagnosis for stator ITSC (inter-turn short-circuit) faults can prevent a fault from expanding into phase-to-phase or ground short-circuits. The TCU (traction control unit) controls the traction inverter to output SPWM (sine pulse width modulation) excitation voltage when the traction motor is at a standstill. Three ITSC fault diagnostic conditions are based on different IGBTs’ control logics. The Goertzel algorithm is used to calculate the fundamental current amplitude difference Δi and phase angle difference Δθ of equivalent parallel windings under the three diagnostic conditions. The six parameters under the three diagnostic conditions are used as features to establish an ITSC fault diagnostic model based on the random forest. The proposed method was validated using a simulation experimental platform for the ITSC fault diagnosis of EMU traction motors. The experimental results indicate that the current amplitude features Δi and phase angle features Δθ change obviously with an increase in the ITSC fault extent if the ITSC fault occurs at the equivalent parallel windings. The accuracy of the ITSC fault diagnosis model based on the random forest for ITSC fault detection and location, both in train and test samples, is 100%.
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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 (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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Elez, Ante, Josip Študir, and Stjepan Tvorić. "Application of Differential Magnetic Field Measurement (DMFM method) in winding fault detection of AC rotating machines as part of expert monitoring systems." Journal of Energy - Energija 67, no. 3 (2022): 3–8. http://dx.doi.org/10.37798/201867367.
Full textAbstract:
Stator and rotor winding damages in rotating machines are result of electrical, mechanical, and thermal stress. Online magnetic field monitoring via permanently installed measuring coils inside air gap is a well-established methodology which enables winding fault detection. The paper deals with a new method for detection of stator and rotor winding inter-turn short circuits of synchronous machines and slip rings induction machines, as well as rupture of rotor bars and cage ring of induction machines. The method novelty is based on differential measurement of magnetic field by using two serial connected measuring coils. They are installed on the places (stator or rotor teeth) in the machine which have, by absolute value, equal magnetic vector potential. The distance between the measuring coils is n·tp, where tp is a pole pitch, and n = 1, 2, 3, 4,... is a multiple of the pole pitch. Measuring the coil-induced voltage enables us to detect stator and rotor winding faults, which means that measured voltage is approximately zero without fault and increases in the presence of fault. Analysis of the measuring signal allows us to detect and locate fault. With this new method it is possible with high sensitivity to determine winding fault, which enables more reliable fault detection. For example, in comparison with the motor current signature analysis method (the most widely used method for motor faults detection), this new method gives 200 times higher sensitivity to fault occurrence. Also, by using the DMFM method, faults can be detected in the time domain and there is no need for spectral or other complex signal analysis. This is very important because the measuring equipment used for machine fault detection can be simple and more economically acceptable. The DMFM method enables fault detection for even small machines with small expense in a very effective way. The only downside of the DMFM method is the fact that machine should be disassembled in order to install measuring coils. This problem is solved during the machine overhaul or during the manufacturing of the machine, when sensors can be easily implemented in the machine. For machines with large air gap, measuring coils can be installed without a machine disassembly. For the purpose of the method testing, numerous finite-element (FE) simulations on the 2- and 3-D machine models have been carried out to verify the method. Powerful numerical tools generate realistic results with properly selected starting and boundary conditions. By FEM models, actual machines with embedded measuring coils where created and simulated. The voltage induced inside the measuring coils is calculated for different machine states, load point and with and without a fault (broken rotor bar or inter-turn short circuit). Also, this method was experimentally validated via series of laboratory tests performed on the real electric machines specially designed for fault study (broken rotor bars, broken ring and inter-turn short circuits in a stator and rotor winding). Additionally, this method is applied on more than 20 real machines in industry. Due to the large amount of measured data, in this paper, it will be presented only one measurement performed on an induction motor on which we have detected one broken rotor bar. The thickness of the measuring coil designed in the printed circuit board technique is 0.3 mm. The number of turns is from 3 to 10. This new method and performed FEM calculations together with the experimental measurements improve fault detection portfolio knowledge that can be used in monitoring and diagnostics of rotating machines. Furthermore, this patent-pending method is already implemented in three innovative products placed on market (expert monitoring systems), so this method is fully confirmed in practice.
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Wang, Luo, Yonggang Li, and Junqing Li. "Diagnosis of Inter-Turn Short Circuit of Synchronous Generator Rotor Winding Based on Volterra Kernel Identification." Energies 11, no. 10 (2018): 2524. http://dx.doi.org/10.3390/en11102524.
Full textAbstract:
The inter-turn short circuit is a common fault in the synchronous generator. This fault is not easily detected at early stage. However, with the development of the fault, it will pose a threat to the safe operation of the generator. To detect the inter-turn short circuit of rotor winding, the feasibility of identifying the stator branch characteristics of synchronous generator during inter-turn short circuit was analyzed. In this paper, an on-line fault identification method based on Volterra kernel identification is presented. This method uses the stator branch voltage and stator unbalance branch current collected from the generator as input and output signals of the series model. Recursive batch least squares method is applied to calculate the three kernels of Volterra series. When the generator is in normal state or fault state, the Volterra kernel will change accordingly. Through the identification of the time-domain kernel of the nonlinear transfer model, the inter-turn short circuit fault of the synchronous generator is diagnosed. The correctness and effectiveness of this method is verified by using the data of fault experimental synchronous generator.
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Wolkiewicz, Marcin, Grzegorz Tarchała, and Czesław T. Kowalski. "Stator windings condition diagnosis of voltage inverter-fed induction motor in open and closed-loop control structures." Archives of Electrical Engineering 64, no. 1 (2015): 67–79. http://dx.doi.org/10.1515/aee-2015-0007.
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AbstractThis paper deals with detection of the stator windings shorted turns in an induction motor drive working under open (scalar) and closed loop (Direct Field Oriented DFO) control structures. In order to detect the early stage of stator winding fault, the analysis of symmetrical and principal components of stator voltages and currents is used. Experimental results obtained from a specially prepared induction motor are presented.
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Xiao, Hai Yan, Xue Wu Wang, and Deng Feng Chen. "Fault Diagnose of Rotor Winding Inter Turn Short Circuit in Generator." Applied Mechanics and Materials 321-324 (June 2013): 1290–94. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1290.
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Based on magnetic characteristics of inter turn circuit of rotor windings in turbo generator, and analysis the principles of detecting coil method. Based on the symmetry of generator structure and magnetic character between stator and rotor, three fault diagnose methods are presented in this paper: Wave form limitations, Half-wave sliding superposition and Reference wave forms. The application example shows that Reference wave forms method is capable of diagnosing inter turn fault, its severity, and especially locating the inter turn short circuit fault.
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El Merraoui, Khadidja, Abdellaziz Ferdjouni, and M’hamed Bounekhla. "Real time observer-based stator fault diagnosis for IM." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (2020): 210. http://dx.doi.org/10.11591/ijece.v10i1.pp210-222.
Full textAbstract:
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.
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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 (2019): 2–19. http://dx.doi.org/10.3311/ppee.14306.
Full textAbstract:
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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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 (2022): 8688–93. http://dx.doi.org/10.48084/etasr.4923.
Full textAbstract:
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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El-Sebah, Mohamed I. Abu, and Faeka M. H. Khater. "A Proposed Fault Diagnostics Technique for Induction Motor Stator Winding." International Journal of Electrical Engineering and Computer Science 5 (May 12, 2023): 33–40. http://dx.doi.org/10.37394/232027.2023.5.5.
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Online monitoring is widely used for induction motor fault diagnostics. This article presents a fault diagnostics technique for a 3-phase induction machine. The proposed technique was developed with fuzzy logic applied to a simplified induction motor model affected by the stator winding short turns. Based on the 3-phase time-domain model, the machine winding with different fault conditions has been simulated to check the resulting speed, torque, and stator current spectrum in each case. The results indicate that the developed fault diagnostics scheme is efficient to specify the fault type of the induction machines stator.
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Liang, Yong Chun. "Analysis of PM Inter-Turn Fault Based on LabVIEW and FFT." Applied Mechanics and Materials 333-335 (July 2013): 1597–600. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1597.
Full textAbstract:
Inter-turn fault is the most probably fault in permanent motor. It is important to find the inter-turn fault and protect the permanent motor. Stator windings current is used to evaluate the inter-turn fault. A small resistance is connected with the permanent motor. The stator windings current is transferred to voltage and this signal is send to data acquisition system and transferred to digital signal. LabVIEW is used to control the data acquisition system and receive the digital signal. FFT is used to analyze the frequency character of the stator windings current and find the character of the inter-turn fault. Test shows that the permanent experimental system consisting of computer and LabVIEW can fully meet the requirements of permanent fault test and research.
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Chen, Peng, Ying Xie, and Daolu Li. "Thermal Field and Stress Analysis of Induction Motor with Stator Inter-Turn Fault." Machines 10, no. 7 (2022): 504. http://dx.doi.org/10.3390/machines10070504.
Full textAbstract:
Inter-turn fault (ITF), a typical motor fault, results in significant variations in the thermal characteristics of a motor. For fault, temperature rise (TR) experiments and thermal field-stress field simulations of an induction motor are carried out to reveal the fault characteristics related to ITF. First, based on the actual structure and the cooling type of the motor, a whole-domain simulation model of the fault thermal field was established. The reasonable equivalence of the motor and the calculation of the heat transfer boundaries were conducted during the modeling process. Then, the three-dimensional transient thermal field under a rated load before and after the fault was obtained, and the accuracy of the simulation could be validated through the comparison of the measured TR at several temperature-measuring points. The heat-transfer law and the notable thermal characteristics of the fault can be presented by analyzing the simulated and measured temperature data. In addition, a fault feature is proposed to provide a reference for diagnosis using the temperature difference of winding at different positions at different moments. Finally, the rotor thermal stress distribution of the normal and faulty motor is obtained by thermal-stress-coupled calculation, which can be used to evaluate the possibility of rotor fault caused by ITF.
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Rajamany, Gayatridevi, Sekar Srinivasan, Krishnan Rajamany, and Ramesh K. Natarajan. "Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network." Journal of Electrical and Computer Engineering 2019 (December 11, 2019): 1–11. http://dx.doi.org/10.1155/2019/4825787.
Full textAbstract:
The intention of fault detection is to detect the fault at the beginning stage and shut off the machine immediately to avoid motor failure due to the large fault current. In this work, an online fault diagnosis of stator interturn fault of a three-phase induction motor based on the concept of symmetrical components is presented. A mathematical model of an induction motor with turn fault is developed to interpret machine performance under fault. A Simulink model of a three-phase induction motor with stator interturn fault is created for extraction of sequence components of current and voltage. The negative sequence current can provide a decisive and rapid monitoring technique to detect stator interturn short circuit fault of the induction motor. The per unit change in negative sequence current with positive sequence current is the main fault indicator which is imported to neural network architecture. The output of the feedforward backpropagation neural network classifies the short circuit fault level of stator winding.
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Ahmed, Thamer Radhi, and Hussein Zayer Wael. "Faults diagnosis in stator windings of high speed solid rotor induction motors using fuzzy neural network." International Journal of Power Electronics and Drive System (IJPEDS) 12, no. 1 (2021): 597–611. https://doi.org/10.11591/ijpeds.v12.i1.pp597-611.
Full textAbstract:
The paper deals with faults diagnosis method proposed to detect the interturn and turn to earth short circuit in stator winding of three-phase highspeed solid rotor induction motors. This method based on negative sequence current of motor and fuzzy neural network algorithm. On the basis of analysis of 2-D electromagnet field in the solid rotor the rotor impedance has been derived to develop the solid rotor induction motor equivalent circuit. The motor equivalent circuit is simulated by MATLAB software to study and record the data for training and testing the proposed diagnosis method. The numerical results of proposed approach are evaluated using simulation of a three-phase high-speed solid-rotor induction motor of two-pole, 140 Hz. The results of simulation shows that the proposed diagnosis method is fast and efficient for detecting inter-turn and turn to earth faults in stator winding of high-speed solid-rotor induction motors with different faults conditions.
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Romdhane, Mabrouka, Mohamed Naoui, and Ali Mansouri. "PMSM Inter-Turn Short Circuit Fault Detection Using the Fuzzy-Extended Kalman Filter in Electric Vehicles." Electronics 12, no. 18 (2023): 3758. http://dx.doi.org/10.3390/electronics12183758.
Full textAbstract:
To avoid damaging the motor and its surrounding equipment, detecting Inter-Turn Short Circuit (ITSC) faults in Permanent Magnet Synchronous Motors (PMSMs) applied in electric vehicles is a crucial task. In this paper, the detection of ITSC faults in stator winding for PMSMs is carried out by means of the Extended KALMAN Filter (EKF) algorithm combined with the Fuzzy Logic Estimator (FLE). To estimate the motor parameters, including the rotor position and speed, the EKF algorithm uses the measured stator currents and voltages beside the stator resistance, which is calculated in advance using fuzzy logic and fed to the EKF. The change behaviors of the estimated parameters were then used to detect short circuit faults in the PMSM. Using Matlab/Simulink, the proposed FL-EKF algorithm was implemented and tested on a faulty PMSM controlled by Field Oriented Control (FOC). The observation of a perfect estimation of the stator resistance through the simulation helps to precisely detect the failure, and that demonstrates the sensitivity and robustness of the proposed approach.
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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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Tarchała and Wolkiewicz. "Performance of the Stator Winding Fault Diagnosis in Sensorless Induction Motor Drive." Energies 12, no. 8 (2019): 1507. http://dx.doi.org/10.3390/en12081507.
Full textAbstract:
This paper deals with the diagnosis of stator winding inter-turn faults for an induction motor drive operating without a speed sensor in a speed-sensorless mode. The rotor direct field oriented control structure (DFOC) was applied, its reference current and voltage component values were analyzed, and their selected harmonics were applied as effective fault indicators. To ensure robust speed estimation, a sliding mode model reference adaptive system (SM-MRAS) estimator was selected. The influence of load torque, reference speed, proportional-integral (PI) controller parameters, and short-circuit current on fault diagnosis and speed estimation performance was verified. Experimental test results obtained for a 3 kW induction motor drive are included.
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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.
Full textAbstract:
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.