Inter-turn Short Circuit Fault Research Articles

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

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

Stator Faults Detection on Induction Motors Using Harmonic Sequence Current Components Analysis

In this paper we propose a strategy for detecting interturn short circuits on stator windings of three-phase induction motors (IM). The strategy is based on an analytical model that includes a stator fault in any of the phase windings considering the h-th harmonic in the supply voltage. To find the adequate model, we propose an equivalent electrical circuit of the IM in steady state, considering each sequence component. The proposed strategy allows the detection of an interturn short-circuit fault by monitoring the fifth-order harmonic component. We present simulation and experimental results for the faulty IM and different operating conditions. In the laboratory tests, we use a programmable power supply in order to synthesize distorted and unbalance voltages. Also, the windings of a three-phase IM have been modified to generate stator faults and validate the proposed model. The results show that the sustained increase of the fifth-order harmonic of positive-sequence current is a good indicator of the fault severity.

Transformer inter-turn winding fault detection based on no-load active power loss and reactive power

An inter-turn fault in a transformer can cause severe damage to transformer windings if not detected quickly. In this paper, the fault diagnosis is discussed by examining the no-load active power loss (NLAPL) and no-load reactive power (NLRP) of the transformer. Two indices, Total harmonic distortion of active power (THDAP) and Total harmonic distortion of reactive power (THDRP), based on odd harmonic components of NLAPL and NLRP, are proposed. To validate the obtained results, the acquired outcomes of tests on a physical device are compared with the results of the finite element analysis. To do this, three-dimensional model of a single-phase transformer is simulated in the transient mode using ANSYS MAXWELL 17.02. Results show that in the presence of an inter-turn short circuit fault on the transformer high voltage (HV) windings, the low voltage side current and NLAPL increase while the variation in NLRP is very small. In addition, from the transient analysis point of view, it becomes clear that THDAP and THDRP are reduced in the presence of an inter-turn fault.

Electromagnetic Torque Characteristics Analysis of Nuclear Half-Speed Turbine Generator with Stator Winding Inter-Turn Short Circuit Fault

To research the variation characteristics and influence factors of air gap magnetic field and electromagnetic torque of nuclear power half-speed turbine generator with stator winding inter-turn short circuit, the two-dimensional transient electromagnetic field of generator-external circuit-grid coupling mathematical model is established by field-circuit-network coupling method. Taking the national laboratory’s generator as an example, and comparing the simulation results of the inter-turn short circuit of the armature winding with experimental data, the correctness of the model is validated. On this basis, the simulation model of a 1407MVA nuclear power half-speed turbine generator is built. Then the stator winding inter-turn short circuit fault is simulated when the generator is connected to the grid with rated load. The characteristics of air gap flux density and electromagnetic torque are obtained before and after fault, as well as, the relationship between their each component and the fault degree is obtained. At the same time, the variation law of air gap flux density and electromagnetic torque is obtained when the relative position between fault winding and magnetic pole changes. The theoretical reference for fault diagnosis and localization using harmonic electromagnetic torque after fault is provided.

A Novel Fault Diagnosis Technique of Interturn Short-Circuit Fault for SRM in Current Chopper Mode

The interturn short-circuit fault of stator winding is a common failure mode in switched reluctance motors (SRMs), which can severely harm the operation of the motor. In this article, a novel method for diagnosing the interturn short-circuit fault in the current chopping mode is proposed, which is suitable for online monitoring and protection of SRMs. The proposed method achieves a fast response and high sensitivity and can mitigate the interference of load conditions on detection reliability. Moreover, this method is based on phase currents, which does not require additional sensor or detection circuits. Meanwhile, the effects of interturn fault location in the stator winding on the motor flux and motor parameters are analyzed in detail through finite-element analysis, which is helpful to establish a reliable diagnostics technique for various fault conditions. Experimentations based on an 8/6 four-phase SRM with special windings designed to simulate interturn short circuit conditions are conducted, and the results have verified the effectiveness of the proposed method.

Diagnosis of Interturn Short-Circuit Faults in Permanent Magnet Synchronous Motors Based on Few-Shot Learning Under a Federated Learning Framework

A large amount of labeled data are important to enhance the performance of deep-learning-based methods in the area of fault diagnosis. Because it is difficult to obtain high-quality samples in real industrial applications, federated learning is an effective framework for solving the problem of sparse samples by using the distributed data. Its global model is updated by the local client without sharing data at each round. Considering computing resources and communication loss of multiple clients, an efficient method based on stacked sparse autoencoders (SSAEs) and Siamese networks is proposed to detect interturn short-circuit (ITSC) faults in permanent magnet synchronous motors. In this article, to achieve an accurate ITSC fault detection, an SSAE was employed to extract sparse features in a limited number of samples, and Siamese networks were used to determine the similarity between the given samples. The problem of fault diagnosis is transformed into a classification problem under few-shot learning. Furthermore, the proposed method is trained successfully in the frameworks of centralized learning and decentralized structure. The experimental results indicate that the proposed method achieved high fault diagnosis accuracy. Moreover, it is suitable for deployment in smart manufacturing systems.

Research on Interturn Short-Circuit Fault Indicators for Direct-Drive Permanent Magnet Synchronous Motor

In this article, the effects produced by interturn short-circuit faults (ISFs) on variables of the direct-drive permanent magnet synchronous motors (DDPMSMs) are investigated. A finite-element method model is established to calculate the motor variables of DDPMSM that includes average torque, second-harmonic torque, torque ripple, branch differential current, branch residual current, branch voltage, and so on. Then, the motor variables under different cases, including various fault conditions and various operating conditions, are analyzed to understand the effects of ISF. Besides, the fault indicators (FIs) are selected based on the analysis of ISF. Finally, the simulation and experimental results are compared at different fault conditions and operating conditions to fully verify the analysis of ISF and FIs. This work provides a significant reference for the fault diagnosis, fault postprocessing, and fault-tolerant control of DDPMSM.

Procedure for Detection of Stator Inter-Turn Short Circuit in AC Machines Measuring the External Magnetic Field

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

Diagnosis of Fault in Doubly-Fed Three-Phase Induction Generator in Wind Power Applications

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

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

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

Intelligent Data-Driven Diagnosis of Incipient Interturn Short Circuit Fault in Field Winding of Salient Pole Synchronous Generators

This article examines if machine learning (ML) and signal processing can be used for online condition monitoring to reveal interturn short circuit (ITSC) fault in the field winding of salient pole synchronous generators (SPSG). This was done by creating several ML classifiers to detect ITSC faults. A dataset for ML was built using power spectral density of the air-gap magnetic field extracted by fast Fourier transform, discrete wavelet transform energies, and Time Series FeatuRe Extraction based on Scalable Hypothesis tests (TSFRESH) to extract features from measurements of SPSG operated under several different severities of ITSC fault. Using this dataset, a wide range of classifiers were trained to detect the presence of ITSC faults. The classifiers evaluated were logistic regression, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -nearest neighbors, radial basis function support vector machine (SVM), linear SVM, XGBoost decision tree forest, multilayer perceptron, and a stacking ensemble classifier including all of the aforementioned. The classifiers were optimized using hyperparameter grid searches. In addition, some feature selection and reduction algorithms were assessed such as random forest feature selection, TSFRESH feature selection, and principal component analysis. This resulted in a classifier capable of detecting 84.5% of samples containing ITSC fault, with a 92.7% chance that fault detections are correct.

Robust Stator Winding Fault Detection in PMSMs With Respect to Current Controller Bandwidth

Permanent magnet synchronous machines work with controllers and controller parameters affect the machine's voltages and currents and, thus, the diagnosis of stator winding fault. One such parameter is the current controller bandwidth that affects the harmonic content in these signals. In the literature, the second harmonics in both dq-axis currents and voltages have been used for fault detection, and the main advantage is that interturn short-circuit (ITSC) winding fault with a few shorted turns, specially one turn, can be detected. One indicator that combines the fault information in voltages and currents is the instantaneous reactive power (IRP). However, it has been shown that IRP does not perform robustly with respect to current controller bandwidth. The main contribution of this article is that it proposes and validates a new mathematical function for ITSCs detection, Rayleigh quotient, which combines the fault information present in voltages and currents to make ITSCs detection robust to current controller bandwidth. The validations from both cosimulation and experiment show that the proposed method is robust to the bandwidth and can detect ITSC faults faster than IRP.

Interturn Short-Circuit Fault Detection of a Five-Phase Permanent Magnet Synchronous Motor

Interturn short circuits are a common fault of permanent magnet synchronous motors (PMSMs). This paper proposes a new method to detect the interturn short-circuit fault (ISCF) of a five-phase PMSM. The method first takes the command voltage and measured current of each phase winding as the original signal and then obtains the delay signal orthogonal to the original signal via Hilbert transform. Then, the generalized instantaneous reactive power of each phase can be calculated from the orthogonal voltage and current signals of each phase. Finally, the influence of the ISCF on the generalized instantaneous reactive power of each phase is analyzed under different working conditions. By comparing the difference in the generalized instantaneous reactive power of each phase, it can be determined which phase winding has the ISCF. The proposed method is verified by simulated and experimental results.

Discrimination Method of Interturn Short-Circuit and Resistive Unbalance Faults for Synchronous Condenser

In this paper, a discrimination method is proposed for the interturn short-circuit and resistive unbalance faults of the synchronous condenser. The mathematical models of the synchronous condenser are established under these two faults. Especially, the zero-sequence voltage components (ZSVC) under two faults are analyzed. Due to the direct connection between the synchronous condenser and the power grid, the fundamental component of ZSVC under the interturn short-circuit fault is not affected by the stator currents, while the resistive unbalance fault is just the opposite. Hence, the amplitude of fundamental component in ZSVC is used to detect the fault occurrence. Then, the change of the fundamental component in ZSVC is observed by changing the excitation current to discriminate the fault type. The effectiveness of the proposed method is verified by the simulation and experimental results.

Numerical Modeling, Electrical Characteristics Analysis and Experimental Validation of Severe Inter-Turn Short Circuit Fault Conditions on Stator Winding in DFIG of Wind Turbines

Inter-turn short circuit fault (ITSCF) can be a great threat for the safety and effectiveness of the generator. In this paper, a simulation model with the experimental platform is established to diagnose the ITSCF in stator windings. A simulation model based on the Finite Element Method (FEM) is designed in ANSYS. Stator current is used for calculating the Root-Mean-Square (RMS) value, phase difference, symmetrical components, Park’s vector trajectory’s and Total Harmonic Distortion (THD). It was found that when ITSCF happened, three phase currents in stator is no longer symmetrical. The ratio of negative to positive sequence of current raises from 0.40 to 14.18 in simulation and 0.52 to 19.89 in the experiment from normal condition to 7 turns shorted. The same trend was seen in Park’s vector trajectory analysis where the eccentricity raises from 0.03 to 0.65 in simulation and 0.14 to 0.75 in the experiment. Phase difference of the three phases is at 120° degree deviation as the ITSCF occurs in stator winding. THD also increased with the increase in the fault level. The results show a good consistency of simulation with experimental results. It was found that the analysis performed in this research are major indications for serious fault in DFIG.

Research on Five-Loop Simplified Scaling Model for Dry-Type Air-Core Reactors With Inter-Turn Short Circuit Fault

In order to simplify the research method of the spatial magnetic field distribution (SMFD) of dry air-core reactors (DARs) in the case of inter-turn short circuit fault, the concept of simplified scaling model was put forward, and the optimal simplified scaling model for different inter-turn short circuit fault was calculated by using the least square method. Firstly, in order to simplify the calculation, 7 typical directions describing the SMFD of DARs were determined based on the similarity degree of magnetic induction intensity in each direction. Then, the simplified scaling model parameters corresponding to different short circuit positions and degrees were studied, and the variation rule was described by using the method of fitting function. Finally, the original model and the simplified scaling model of the DARs in the case of inter-turn short circuit were established respectively in the laboratory. The results show that the optimal structure relative height obtained by experiment is 30% and 20% respectively, which is the closest to the parameters obtained by computer simulation, i.e.,28% and 20.3%.

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.