Inter-turn Short Circuit Fault Research Articles

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

This work proposes an alternative strategy to the use of a speed sensor in the implementation of active and reactive power based model reference adaptive system (PQ-MRAS) estimator in order to calculate the rotor and stator resistances of an induction motor (IM) and the use of these parameters for the detection of inter-turn short circuits (ITSC) faults in the stator of this motor. The rotor and stator resistance estimation part of the IM is performed by the PQ-MRAS method in which the rotor angular velocity is reconstructed from the interconnected high gain observer (IHGO). The ITSC fault detection part is done by the derivation of stator resistance estimated by the PQ-MRAS estimator. In addition to the speed sensorless detection of ITSC faults of the IM, an approach to determine the number of shorted turns based on the difference between the phase current of the healthy and faulty machine is proposed. Simulation results obtained from the MATLAB/Simulink platform have shown that the PQ-MRAS estimator using an interconnected high-gain observer gives very similar results to those using the speed sensor. The estimation errors in the cases of speed variation and load torque are almost identical. Variations in stator and rotor resistances influence the performance of the observer and lead to poor estimation of the rotor resistance. The results of ITSC fault detection using IHGO are very similar to the results in the literature using the same diagnostic approach with a speed sensor.

Detection of Stator Interturn Short-Circuit Faults in Inverter-Fed Induction Motors by Online Common-Mode Impedance Monitoring

By monitoring the online common-mode (CM) impedance of an inverter-fed induction motor (IM) through an inductive coupling technique at a specific frequency of interest (FOI), the stator interturn short-circuit (ITSC) faults of the IM can be detected with good confidence. The inductive coupling technique is adopted because it monitors the CM impedance of the IM without a physical electrical connection, which eliminates electrical safety hazards and facilitates ease of implementation. Using a 1/2-hp squirrel-cage IM as a test case, it has been demonstrated that the proposed method is sensitive to the stator ITSC faults but invariant to motor load and speed variations, as well as bearing and rotor faults, making it highly reliable and robust for stator ITSC fault detection.

Диагностика неисправностей обмотки статора трехфазного асинхронного двигателя с помощью векторного анализа Парка

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.

Thermal Analysis of Low-Power Three-Phase Induction Motors Operating under Voltage Unbalance and Inter-Turn Short Circuit Faults

Three-phase induction motors are considered to be the workhorse of industry. Therefore, induction motor faults are not only the cause of users’ frustrations but they also drive up the costs related to unexpected breakdowns, repair actions, and safety issues. One of the most critical faults in three-phase induction motors is related to the occurrence of inter-turn short circuits, due to its devastating consequences. The topic of inter-turn short-circuit faults in three-phase induction motors has been discussed over recent decades by several researchers. These studies have mainly dealt with early fault detection to avoid dramatic consequences. However, they fall short of addressing the potential burnout of the induction motor before the detection step. Furthermore, the cumulative action played by an inevitable degree of unbalanced supply voltages may exacerbate such consequences. For that reason, in deep detail, this paper delves into the thermal analysis of the induction motor when operating under these two harsh conditions: unbalanced supply voltages and the presence of the most incipient type of inter-turn short-circuit condition—a short-circuit between two turns only. In this work, the finite element method has been applied to create the faulty scenarios, and a commercial software (Flux2D) has been used in order to simulate the electromagnetic and thermal behavior of the machine for various degrees of severity of the aforementioned faulty modes. The obtained results confirm that the diagnostic tools reported in the literature might not be effective, failing to warrant the required lead time so that suitable actions can be taken to prevent permanent damage to the machine.

WITHDRAWN: Fault diagnosis of three-phase electrically-excited synchronous motor by using adaptive threshold algorithm

Abstract Stator winding inter-turn short circuit faults and single-phasing faults are the major causes of failures of Electrically-Excited Synchronous Motor. This abnormality progressively grow faults leads to be complete damage of the machine. Hence in order to improve the sustainability of the system and increase the life of the motor suitable condition monitoring technique are necessary to identification of fault occurrence. In this context, this paper introduce the detection and phase identification of the turn-level short circuit faults in electrically excited synchronous machines. To detect and identify the faulty phases an algorithm is proposed based on a wavelet based multi-resolution analysis along with adaptive threshold for motor operated under different operating conditions. Stationary wavelet transform is employed to get the 3-ɸ currents fault residues and then discrete wavelet transform is employed to extract the disturbance information from the 3-ɸ residue currents. In order to find the fault location and phase identification fault index and 3-ɸ energies are compared with adaptive thresholds. Under different operating conditions finally, the algorithm is tested with practical data for single-phasing and various levels of inter-turn short circuit faults. By using acquired practical results the validity and successfulness of a proposed algorithm is clearly determined.

Finite element modeling of asynchronous electric motors with electrical defects of stator and rotor

The paper presents results of finite element modeling of induction motor in the growth of stator and rotor fault. Due to the symmetry design of the electric motors relative to the rotor axis the simulation problem is solved in a plane orthogonal axicon. Obtained data the characteristics variation of induction motors with defects: broken rotor bars faults and stator inter-turn short-circuit fault. The results of finite element analysis show the processes occurring in the electric motor for defects and growth. The applied results of modeling are the identification of characteristic diagnostic property there are defects and growth, as well as the development of methods for diagnosing the technical condition of the induction machine

Improving the Accuracy of Wound-Rotor Resolvers Under Inter-Turn Short Circuit Faults

The performance of the resolver as a position sensor is significantly influenced by electrical and mechanical faults. Among different faults, short circuit in the stator windings has more destructive effect than others on accuracy of resolver. Furthermore, due to thin wires of the stator it is highly likely to occur. Therefore, in this paper a robust design is proposed for wound-rotor resolvers to not only suppress the influence of short circuit fault in the stator/rotor winding, but also improve the accuracy of the sensor under mechanical faults. The effectiveness of the proposed robust design is demonstrated by an analytical model based on winding function method and then approved by time stepping finite element analysis (TSFEA) and experimental measurements on a prototype sensor.

Skewness Scanning for Diagnosis of a Small Inter-Turn Fault in Quadcopter’s Motor Based on Motor Current Signature Analysis

This paper deals with hall sensor based very small inter-turn short circuit fault assessment of single-phase quadcopter system using current signature analysis of connected brushless DC motors at the transient condition. Firstly, a quadcopter has been modeled with a proportional-differential controller, and roll, pitch, and yaw angles have been adjusted as reference inputs. Secondly, the current signal of each motor has been captured through a hall sensor for monitoring at various conditions. Then, Skewness scanning is performed on multi-resolution analysis based wavelet coefficients obtained from the starting transients. Variation in the asymmetrical distribution of coefficients has been observed at different decomposition levels. Levels that show almost linear variation with the increase of fault are selected for fault detection. An algorithm of Skewness scanning has been proposed for the detection of the number of shorted turns present in the system. Practical validation of the work has been done with data captured by the data acquisition system through the Hall probe; satisfactory diagnosis has been observed in experimentation.

Modeling and fault diagnosis of multi-phase winding inter-turn short circuit for five-phase PMSM based on improved trust region

Multi-phase Permanent Magnet Synchronous Motors (PMSMs) are widely used in fault-tolerant control applications. As an electrical fault with a high probability, inter-turn short circuit (ITSC) fault seriously affects the reliability of five-phase PMSM. However, due to high coupling of fault parameters, most of the fault models and diagnosis methods in fault-tolerant control focus on one-phase winding ITSC fault of three-phase PMSM. In this paper, a novel model and a fault diagnosis method are proposed for multi-phase winding ITSC (MP-ITSC) of five-phase PMSM. First, by analysing the related physical parameters of the motor, a mathematic model of MP-ITSC fault is established. Then, a parameter estimation method based on improved trust region (ITR), which accelerates the convergence speed of trust region algorithm and reformulates the fault diagnosis as the extreme seeking for fault parameters is proposed to detect the ITSC fault level. Experimental results provide verification of the proposed model and fault diagnosis method.

Fault-Tolerant Control of Coil Inter-Turn Short-Circuit in Five-Phase Permanent Magnet Synchronous Motor

In the five-phase permanent magnet synchronous motor (PMSM) control system, the torque ripple caused by coil inter-turn short-circuit (ITSC)fault will make the motor performance worse. Due to the existence of the short-circuit current in the faulty phase and the third harmonic component in the permanent magnet flux linkage, the electromagnetic torque will contain even-order ripple components when the faulty phase is removed. Torque ripple also cause speed ripple. In this paper, the repetitive controller (RC) is used to perform proportional gain compensation for speed ripple. By designing the RC and connecting RC and proportional integral (PI) controller in parallel for the speed loop, the torque ripple amplitude can be reduced. It can be seen from the simulation and experimental results that the torque ripple suppression strategy based on RC can effectively suppress the torque ripple under ITSC fault.

Stator End-Winding Thermal and Magnetic Sensor Arrays for Online Stator Inter-Turn Fault Detection

In this paper, two novel stator inter-turn fault sensing schemes, based on infrared (IR) thermopile sensor array (IRSA) and Hall-effect sensor array (HESA) are proposed. These circular sensing arrays are mounted along the inner wall of the stator's casing facing the end-winding region. This enables a direct measurement of temperature and magnetic flux distribution along the end-winding region in a non-contact way. Thus, the deviation in thermal and magnetic symmetry introduced by an inter-turn short circuit fault can be readily assessed. The proposed sensing schemes provides an intuitive and straight forward approach to inter-turn fault monitoring compared to the conventional methods, which depend on detecting fault induced secondary effects on external signals (motor currents, casing vibration, and temperature). The practicality and the diagnostic ability of the end-winding sensor array approaches in detecting the stator inter-turn faults is demonstrated using a 1.5 kW induction motor test rig. Compared to IRSA, HESA is found to be more versatile and with better early fault detection capability. Hence, an embedded online monitoring algorithm based on HESA is developed and demonstrated using the test rig.

Mathematical model and diagnosis of rotor fault in synchronous condenser

With the application of new energy in grid-connected system and the development of UHV DC transmission, the grid's requirements for reactive power regulation have gradually increased. Considering that, large-scale synchronous condensers have been put into use again. However, it is difficult to extract the characteristic signal of the inter-turn short-circuit fault in rotor windings of synchronous motors. In order to improve the stability of condensers, a certain relationship between the excitation current and the number of turns is derived using the Parker equation in the dq0 coordinate system, and the differential equation simulates the excitation current. Then the characteristic energy value of the fault signal is extracted through wavelet packet decomposition and reconstruction, and it is input to the RBF neural network for fault diagnosis. It is proved by MATLAB simulation that the diagnostic method proposed in this paper can effectively detect the degree of short-circuit faults between the turns of the rotor in condensers.

Interturn short‐circuit fault diagnosis in PMSM with partitioned stator windings

A new signal-based strategy for stator winding interturn short-circuit fault detection and isolation in permanent magnet synchronous machines (PMSM) is proposed. The strategy is based on an analysis of the midpoint voltage of the phase windings. A PMSM model with partitioned stator windings including an interturn short-circuit fault is presented. Unlike the motor current signature analysis methods, the midpoint voltage measurement allows detecting an incipient stator fault even under external disturbances without false alarms. For PMSM generators used in variable-speed wind turbines, the random changes of wind produce fluctuations in the rotor speed. In this situation, conventional fault detection methods used for the steady-state condition may produce errors in the fault diagnosis. However, the proposed strategy is capable of rejecting the effects of the non-stationary speed conditions by resampling the voltage signals at fixed increments of the rotor angular position. Several experimental results in time and frequency-domain are presented for different speed conditions using a PMSM with modified stator winding.

Early detection and localization of stator inter-turn faults based on discrete wavelet energy ratio and neural networks in induction motor

This paper proposes an improved diagnosis method for early detection and localization of Inter-Turn Short Circuit (ITSC) faults in the stator winding of the induction motor (IM). The main advantages of the method are the simplicity, low cost, and accurate diagnosis of these types of faults such that it can detect and localize even a low number of shorted turns faults in the stator winding of the motor. This is achieved by using a novel indicator that is based on the Discrete Wavelet Energy Ratio (DWER) of three stator currents, with Artificial Neural Network (ANN). Three different models of typical neural networks, namely, Multi-Layer perceptron (MLP), radial basis function (RBF), and Elman Neural Network (ENN) based on Bayesian Regularized (BR) training algorithm are proposed for ITSC classification based on fault feature extraction using discrete wavelet transform. To test the effectiveness of the proposed method, several experimental tests were carried out under different operating conditions of the IM, which contains the healthy and the ITSC faults cases that have experimented under various loads and different numbers of shorted turns in the three phases of the motor. The obtained results proved that the DWER is an accurate and robust indicator to diagnose the ITSC fault, this is confirmed by ANN results which gave the best results with the Bayesian regularized Elman network model that has the highest performance with minimal error rate is 10−9.Consequently, the combination DWER-ENN has assured its ability to accurately detect high and even low numbers of the shorted turns and localize the defective phase even within various loads in the IM.

A Stray Magnetic Flux-Based Robust Diagnosis Method for Detection and Location of Interturn Short Circuit Fault in PMSM

Early diagnosis of interturn short circuit (ITSC) fault is essential to prevent catastrophic electrical machine failures. Along with the detection, locating the faulty winding at its incipient stage is also critical to reduce the outage duration and operation losses. This article proposes a practical method to detect the ITSC fault and locate the faulty winding in permanent magnet synchronous motors (PMSMs) through stray magnetic flux monitoring. It is shown that relying on the fundamental stray flux component can potentially underperform at certain operating conditions leading to fault negative error. To compensate this shortcoming, the third harmonic component of stray flux is comprehensively analyzed and proposed as a complementary ITSC fault indicator. Furthermore, its reliability over the wide range of motor operation is verified to eliminate potential fault negative errors. It is also shown that the proposed method is independent of the fault severity which is a major advantage for practical applications. The findings are supported through 2-D-FEA PMSM drive system simulations and experiments under two different fault severity cases.

Detection and Discrimination of Incipient Stator Faults for Inverter-Fed Permanent Magnet Synchronous Machines

The online detection of incipient faults for permanent magnet synchronous machine (PMSM) has been a great challenge in recent decades, where the fault features are extremely trivial and the machine fault type is normally unknown in advance. An online fault detection and discrimination of incipient faults for PMSM, including stator interturn short-circuit and resistive unbalance fault, is proposed in this article. First, zero-sequence voltage signal is collected and the zero-sequence voltage component (ZSVC) is used to detect the abnormal status of the PMSM. Then, high frequency (HF) signals are injected to the PMSM to discriminate the fault type. It shows that the amplitude of the HF component in ZSVC is proportional to the frequency of the injected signal under interturn short-circuit while it is constant under the resistive unbalance fault, and due to this difference, the fault type may be discriminated. Finally, the effectiveness of the theoretical analysis is validated by the simulation and experimental results.

A Robust and Practical Approach to Estimate the Number of Shorted Turns in PMSM With ITSC Faults

This article puts forward a novel method to estimate the fault severity index and consequently the number of shorted turns in permanent magnet synchronous motors (PMSMs) with interturn short circuit (ITSC) fault. In this method, the machine is excited with dc current at standstill conditions to obtain the winding resistance seen by the d-axis of the machine. The estimated d-axis resistance contains useful information pertaining to the fault severity index and is used to extract the fault severity index and the number of shorted turns in the faulty motor. The proposed method enables the estimation of fault severity index without complex machine modeling and extensive experiments with different machine prototypes, or finite-element analysis (FEA) models to analyze the relationship between the machine currents and short-circuit current. To enhance the accuracy of the estimation algorithm, this article addresses practical issues associated with inverter nonlinearity effects such as distortion voltage due to deadtime effects and voltage drops across the switching devices and proposes method to estimate the fault severity index that is immune to the aforementioned issues. Determining the fault index is critical in order to design appropriate mitigation techniques, as well as defining a safe operating area for the machine to avoid further damage to the winding. To verify the efficacy of this algorithm, experiments are conducted on a three-phase PMSM with artificial taps that imitate the behavior of ITSC fault, and the results which demonstrate the practicability of this algorithm are presented.

Stator winding fault detection of induction generator based wind turbine using ANN

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.

Online detection method for inter-turn short-circuit fault of permanent magnet synchronous motor based on deep learning

Aiming at the difficult problem of inter-turn short-circuit fault diagnosis of permanent magnet synchronous motors without stopping the machine, this paper takes timing anomaly detection as the starting point, and proposes an online fault detection method based on transfer learning deep self-coding network. First, a migration deep self-coding network model is constructed, and a combined sample of negative sequence current and electromagnetic torque is used to extract common features in different domains. Then, the online detection model of abnormal timing is established. Finally, the permutation entropy value of the normal state of the motor is used to construct an alarm threshold to improve the matching speed of abnormal sequences in the online data. Experimental results show that the method in this paperhas better detection practicality.

Fault coil location of inter‐turn short‐circuit for direct‐drive permanent magnet synchronous motor using knowledge graph

Inter-turn short-circuit fault (ISF) degrades its reliability and may cause serious catastrophes for direct-drive permanent magnet synchronous motor (DDPMSM). Fault location technology can reduce maintenance time, increase the mean time between failure (MTBF), and then improve the reliability of DDPMSM. Hence, an intelligent fault locating system for DDPMSM is proposed in this paper. This system proposes a knowledge graph (KG) based diagnostic tool for detection and location of the fault coil. First, the fault model of the DDPMSM with multiple branches parallel winding is established, which is used to analyze the fault characteristics of motor. Second, the BDC and BRC are proposed as the fault indicator. The effectiveness and robustness of fault indicator are analyzed. Then, the KG system are designed and established according to the relationship between fault indicator and location of fault coil. Finally, the system is tested by data under different fault and operation conditions. The test results showed that the proposed fault locating system can detect and locate the fault coil in early stage. The minimum ratio of shorted turns to branch turns that can be detected is 0.52%. The minimum ratio of shorted turns to branch turns that can be located is 6.25%.