Stator Inter-turn Fault Research Articles

Online Stator Interturn Fault Detection Using Hilbert Transform and Neural Network for Traction Motors of Urban Rail Vehicles

Online Stator Interturn Fault Detection Using Hilbert Transform and Neural Network for Traction Motors of Urban Rail Vehicles

Study on Thermal Characteristics of Winding under Inter-Turn Fault of Oil-Immersed Transformer

The inter-turn fault is a common fault of oil-immersed transformers. Due to the deterioration of inter-turn insulation, its resistance decreases, which leads to an increase in the winding coil current and loss, causing the winding temperature to rise. Therefore, analyzing the electrothermal characteristics of inter-turn short circuit faults in oil-immersed transformers can provide a theoretical basis for the diagnosis of inter-turn faults in transformers. This paper studies the equivalent circuit under inter-turn faults and establishes a field–circuit coupling model to calculate the current characteristics under different inter-turn short circuit faults of transformers. The distribution of coil losses under faults is calculated as thermal source excitations, and a thermal–fluid-coupled calculation model for oil-immersed transformers is established. After the experimental verification, it is found that both the errors between the winding and tank surface temperatures do not exceed the acceptable limits, validating the effectiveness of this model. Using this model to calculate the temperature distribution for various inter-turn short circuit faults in transformers reveals their hot-spot characteristics. The results indicate that under inter-turn fault conditions, the temperature at faulty turns will gradually increase towards a limit, eventually leading to coil damage. This research provides theoretical support for enhancing transformer detection and diagnostic capabilities.

Current and flux correlation analysis for detection, location, and phase identification of stator inter-turn short-circuit fault in doubly-fed induction generator

This study proposes an innovative method for identifying and locating inter-turn short-circuit faults in doubly-fed induction generators (DFIGs), commonly used in wind-energy production. Our approach uniquely integrates data from both current and magnetic-flux monitoring, offering a complete framework for fault diagnostics. We effectively distinguish between normal and abnormal conditions in the generators by observing the patterns in current and flux waveforms. Normal conditions are marked by uniform waveforms, whereas faults create noticeable imbalances. A key feature of our research is the development of a technique to accurately determine which phase of the generator is affected by the fault, facilitating quicker and more effective repairs. To pinpoint the exact location of the fault, we use a set of four magnetic-flux sensors strategically placed around the generator. These sensors detect variations in magnetic flux, enabling the precise location of the fault. By focusing on these three essential elements—fault detection, phase identification, and exact fault localisation—our proposed algorithm significantly improves the dependability of DFIGs in wind-power applications.

Model Based Stator Interturn Fault Analysis in Synchronous Reluctance Motor Drives

Model Based Stator Interturn Fault Analysis in Synchronous Reluctance Motor Drives

Stator Inter-Turn Short-Circuits Fault Diagnostics in Three-Phase Line-Start Permanent Magnet Synchronous Motors Fed by Unbalanced Voltages

This paper addresses the diagnostics of stator faults in three-phase line-start permanent magnet synchronous motors. More traditional fault diagnostic methodologies are unable to properly diagnose stator inter-turn short-circuit faults in three-phase motors under unbalanced supply voltage conditions, since both conditions impact the fault indicators used for inter-turn short-circuit fault diagnostics in a similar way. In this paper, the relation between the symmetrical components of the three-phase quantities and the harmonic components of the Extended Park’s Vector Approach (EPVA) is established. It is proved that the negative component and the EPVA harmonic component at a frequency twice the supply frequency are directly related to the fault occurrence. It is also proved that the healthy motor negative impedance is constant and not load-dependent. Based on this, the negative impedance/admittance is indirectly analyzed, through the combined use of the Extended Park´s Vector Approach of both voltage and current, and is explored for the fault diagnostics. Experimental results, obtained for different load torques, unbalanced supply voltage values, and fault severity levels, show that inter-turn short-circuit fault diagnostics can be achieved even under unbalanced supply voltage conditions based on the analysis of the motor admittance, at a frequency twice the supply frequency, that is the negative sequence admittance.

Stator inter-turn short-circuit fault diagnosis in induction motors applying VI loci-based technique

Due to the susceptibility of low-power three-phase induction motors (IMs) to stator inter-turn short-circuits (ITSC), researchers have been required to develop practical and cost-effective online systems for detecting faults in the stator winding. It is highly recommended to detect faults at a low fault severity level, as the fault current circulating through the shorted turns exponentially increases due to a reduction in insulation resistance and associated insulation degradation. The proposed Voltage–Current (VI) loci-based technique is a non-invasive, less expensive, and fault-sensitive approach that has been scarcely implemented in industries for stator ITSC faults analysis and accurate faulty phase identification, even under low fault severity. The proposed algorithm can compensate for various factors such as unbalanced supply voltages, asymmetries in winding construction, and errors in sensing element calibration. Experimental results presented for a three-phase 1 hp, 415 V SCIM investigate and validate the competency of the proposed work.

Search-Coil Based Stator Interturn Fault Detection in Permanent Magnet Machines Running under Dynamic Condition

Interturn short circuit (ITSC) fault is a common fault in electric machines, which may severely damage the machines if no protective measure is taken in time. There are numerous fault diagnosis methods under a steady-state condition. However, there is relatively limited research on fault diagnosis under dynamic conditions. The dynamic operation of motors, such as in electric cars, is a very common scenario. Hence, this paper proposes a search-coil based online method for detecting ITSC fault in permanent magnet synchronous machine (PMSM) under a dynamic condition. The search coils are placed on the stator circumference at equal intervals. Each search coil reflects the information about the magnetic field in its vicinity and also contains the fault information. In this paper, the voltage induced by the odd sideband harmonics around the even carrier (2ωc±ω0) is selected as the fault characteristic to be used in effectively improving the detected signal-to-noise ratio by excluding the interference of the counter-potential of the permanent magnet. Since two adjacent search coils are placed one pole apart, a set of quadrature signals can be acquired. The Digital Lock-In Amplifier (DLIA) technology is applied to extract the amplitude of the characteristic voltage, which overcomes the shortcomings of the traditional spectrum analysis in applying to non-stationary conditions. The amplitudes of the voltage at different search coils can be compared to further determine the occurrence of a fault and also its rough location if occurred. Experiments were conducted with a six-phase PMSM for demonstrating the effectiveness of the proposed method. The obtained results show that the proposed method can accurately determine the occurrence of a fault.

Matlab sensitivity analysis toolbox: an application on faults identification in induction motors

PurposeThis paper aims to introduce the usage of sensitivity analysis (SA) for the problem of faults identification in three-phase induction motors (IMs). These motors are susceptible to different kinds of faults that should be detected in a proper time to keep the systems working in a safety environment.Design/methodology/approachOne of the effective approaches for faults identifications, which is presented in the literature, is a model-based strategy. This strategy mainly depends on using a software model to make an identification decision. Therefore, this work intends to examine the model sensitivity towards variables’ variation. The SA toolbox of Matlab R2017b package is used for this purpose since the Matlab software is a well-known environment, and it is easy for a nonstatistical person to deal with it. As a study case, open-circuit and stator inter-turn faults in the stator windings of a three-phase IM have been chosen.FindingsThe results show that the model-based strategy is considerably speed up by up to 30% when neglecting the trivial model’s parameters with the same accurate identification decision as compared with the results of this strategy without using the SA.Originality/valueThe novelty of this work is summarized in devoting the usage of SA in the field of faults identification to enhance the speed of final decision.

Detection of the stator inter-turn fault using the energy feature of the wavelet coeffcients obtained through continuous wavelet transform

This research aims to investigate a fault detection method applicable to the stator part of the Brush-Less DC motor (BLDC). Indeed, it is a concern to make sure the motor is operating in a healthy mode, and in any other case, it is of great importance to detect the fault as soon as possible to prevent the further ruin of the major system. Regarding this, a sub-branch method of the Wavelet Transform (WT) analysis, named Continuous Wavelet Transform (CWT), is utilized to observe the short-circuit fault in the stator coils. Thus, a novel simulator of the BLDC motor is developed by making an interconnection between ADAMS and MATLAB in which different electrical and mechanical components are included. Therefore, a close-to-reality model of the BLDC motor is achieved, leading to a more accurate evaluation of the proposed method. An energy-type feature will be suggested to characterize the fault happening. Through acquiring the normalized energy amount for one of the Wavelet Coefficient (WC) signals, obtained by the CWT, and comparing the energy with a predefined threshold amount of energy for that signal, it is feasible to detect the stator's flawed performance. By conducting different simulations, the proposed method will be validated.

A Case for Micromachines in Laboratory-Based DFIG Wind Turbine Systems for Fault Studies

For laboratory-based systems, the selection of components is critical to ensure proper representation of the actual system. In past investigations, researchers made use of small-scale off-the-shelf wound rotor induction machines (WRIMs) to perform laboratory-based studies. However, there are no reports validating their applicability in comparison to using the actual machines. In this regard, this paper investigates the correlation between the dimensional parameters and the electrical asymmetry indicators for a large-scale wind generator and laboratory-based generators. For the latter, a comparison is made between using a standard small-scale off-the-shelf WRIM and a micromachine. It is shown that the harmonic frequencies in a healthy machine and those due to inherent asymmetries appear at the same positions on the stator current spectrum for the three machines. However, there is a difference in the amplitudes of these components, given their dependence on the winding factors for a specific machine. The dimensional analysis techniques used to scale down the large-scale generator into a micromachine ensure that these parameters are related. However, this is not the case for the standard small-scale WRIM. Further work demonstrates the correlation of the fault-related harmonics between the micromachine and the large-scale machine for a rotor inter-turn fault condition. Micromachines are designed to ensure that the time constants are proportional representations of the larger machine, thus providing an accurate reflection of what is expected under transient operating conditions of wind turbines. This would assist in the development of condition monitoring strategies that are suited for the actual systems in the industry.

Voltage–current locus‐based stator winding inter‐turn fault detection in induction motors

SummaryMonitoring the condition of induction motors requires continuous inspection of all motor components. The three‐phase stator windings forming the critical components of the motors and handling heavy currents as high as twice the blocked rotor current under inter‐turn short circuits necessitate a fault detection system that is efficient, appropriate, and worthwhile. The proposed algorithm takes measurements of the three‐phase supply voltages and phase currents and plots their loci for each cycle under various fault levels. The proposed simple, nonintrusive, less expensive, and fault‐sensitive voltage–current loci (£(v–i))‐based technique accurately identifies the faulty phase and detects stator inter‐turn faults (SITFs). In addition, the algorithm corrects supply voltage imbalances, winding asymmetries, and calibration errors of sensing elements. It contributes to examining the insulation condition of the stator windings at the onset and identifying the faulty phase of a motor. The experimental results for a 1 hp, 415 V squirrel‐cage induction motor (SCIM) in a laboratory investigation validate the competence of the proposed technique under various fault levels and load conditions.

Development of a Comprehensive Analytical Model of Induction Motor Under Stator Interturn Faults Incorporating Rotor Slot Harmonics

The objective of this article isto developa comprehensive mathematical model of a three-phase induction motor to facilitate stator interturn (SIT) fault related study. A detailed analytical model that includes accurate inductance modeling considering rotor slot harmonics (RSH) under healthy as well as SIT faults is described. The proposed model is developed analytically in a step-by-step manner incorporating the features of RSH. A detailed but simplified analysis considering both magnetomotive force harmonics and air-gap flux harmonics has been presented. An asymmetric model is proposed, including SIT faults, in a single phase of the induction motor in the next stage of the modeling. The effect of saturation has also been considered in the model. The proposed model is validated by comparing the simulation results in transient, steady state, and frequency domain with the experimental results. Comparison shows a high degree of correlation with the results of the actual motor. The contributions of this work are justified by comparing the proposed model with the existing models. This article also shows that a frequency signature related to RSH present in the line current is capable of characterizing the SIT faults.

Cost-effective process experimental studies on stator inter-turn faults detection in induction motor using harmonic RPVM and decomposition wavelet transform

Abstract This paper presents the development of a new algorithm for the diagnosis of induction motor inter-turn short circuit and mixed combined fault detection based on the reduction Park’s vector modulus and discrete wavelet transform. The proposed method is based on a combination between the RPVM approach and DWT. This new signal of the RPVM is a cost-effective alternative implementation of the Extended Park’s Vector approach through monitoring only the fluctuations of the stator current zero-crossing times in the frequency domain. Using this technique, the characteristic frequency components of the fault are extracted from only one phase stator current when compared to Park’s vector modulus and Hilbert modulus. The main advantages of the RPVM signal are to acquire the values of a single-phase stator current to allow reducing the calculation complexity and for extracting features frequencies of the faults. Thus, the reduced amount of data samples must be stored and processed. This study investigates theoretically by using the proposed RPVM signal for performing to diagnose induction motor faults through the DWT method and processed via energy eigenvalue of energies. This proposed approach RPVM-DWT is validated experimentally to demonstrate its effectiveness in that the proposed algorithm reduces the computational cost and can be used in induction motor fault detections and identifications with different fault severity conditions.

State Estimation MRAS and Identification of Stator Winding Phase Fault Detection of the PMSG in Wind Energy Based on the Sliding Mode Control

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.

A robust principal component analysis-based approach for detection of a stator inter-turn fault in induction motors

Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries. A stator inter-turn fault (SITF) is considered to be the most common electrical failure according to statistical studies. In this paper, an algorithm for the detection of an SITF is presented. It is based on one of the blind source separation techniques called principal component analysis (PCA). The proposed algorithm uses PCA to discriminate between the faulty components of motor current signatures and motor voltage signatures from other components. The standard deviation of one of the decomposed vectors is used as a statistical SITF criterion. The proposed criterion is robust to non-fault conditions including voltage quality problems and large mechanical load changes as well as harmonic contaminants in the voltage supply. In addition, with a straightforward and low computational burden in the fault detection process, the proposed method is computationally efficient. To evaluate the performance of the proposed method, large numbers of practical and simulation scenarios are considered, and the results confirm the good performance, high degree of accuracy, and good convergence speed of the proposed method.

Fault diagnosis for induction generator-based wind turbine using ensemble deep learning techniques

Fault diagnosis in induction machines (IM) require effective detection at early stages to prevent permanent machine failure. This requires fast sensing of disturbances as well as efficient diagnosis methods. Conventional signal measuring techniques are usually intrusive, sensitive, with a low signal-to-noise ratio, and may show limited performance at incipient fault stages. Moreover, traditional machine learning (ML) diagnosis techniques based on handcrafted feature extraction methods suffer from limitations that can be overcome by deep learning (DL) with automatic feature extraction capabilities. In this paper, an online, non-intrusive method is proposed for fault diagnosis in a three-phase IM based on infrared imaging. The proposed method integrates three convolutional neural network (CNN)-based DL models (Inception, Xception, and MobileNet) to identify IM health status, fault types, and stator Inter-turn faults (ITF) location and severity. Deep features obtained from these CNNs are merged through Discrete Wavelet Transform (DWT) resulting in spatial-time-frequency features. This fusion reduces the input features’ size and improves diagnostic accuracy. Merged features’ length is further reduced using the Principal Component Analysis (PCA) where these reduced features are then used for classification. Compared to DL-based diagnosis methods introduced in previous work, the proposed method shows superior performance. First, it uses the IR images directly without the need for clustering and segmentation steps, thus saving time and effort. Moreover, ensembled DL is applied via combining the three CNNs benefits altogether rather than applying an individual DL method. Finally, unlike the huge features’ number exhibited by previous studies, the proposed approach uses minimal features, thus reducing classification complexity and time while maintaining 100% classification accuracy.

A New Approach based on Electromechanical Torque for Detection of Inter-Turn Fault in Permanent Magnet Synchronous Motor

Fault detection is an important issue for permanent magnet synchronous motors (PMSMs). In the initial stage, it is very crucial to detect stator winding inter-turn short-circuit failure, which is one of the most common types of faults. In this paper, a new approach based on electromechanical torque has been proposed to detect the stator inter-turn short circuit fault (ISCF) that occurs in surface-mounted permanent magnet synchronous motors (PMSMs). New fault signatures based on the torque signal that can be used in stator winding ISCF detection are tried to be found in the torque frequency distribution. Fast Fourier Transform (FFT) was used to extract the torque frequency components associated with the stator ISCF. It was found that the amplitudes of the 2nd and 4th harmonic components of the torque signal are distinctive features that can be used for stator winding ISCF detection in PMSM. With the proposed components of the 2nd and 4th harmonic of torque, an inter-turn fault can be easily detected at the initial stage. Both experimental results and simulation results for healthy and three different faulty states (2%, 12.5%, and 25% ISCF) at different load levels and different speeds are presented in this paper.

A Temperature and Magnetic Field-Based Approach for Stator Inter-Turn Fault Detection

Effective fault detection techniques are needed to reduce the maintenance and downtime costs of induction motors. This article presents an approach for stator end-winding inter-turn short-circuit fault detection using a metal-coated fiber Bragg grating (FBG) sensor with 0.5-mm diameter and 10-mm length to monitor the temperature and magnetic field around the end winding. The sensing theory, design, manufacturing, and installation of the sensor are detailed in this article. The nickel coating on the surface of the sensor is formed by electroless plating and electroplating, which makes the sensor sensitive to both temperature and magnetic field. The feasibility of the presented approach has been supported by both simulation and experimental results. The temperature and magnetic field signals are decoupled by filtering, and the experimental data are analyzed in the time-frequency domain. The results show that the inter-turn short-circuit fault can be detected at an incipient stage with 5% shorted turns.

Application of machine learning for inter turn fault detection in pumping system

Pump fault diagnosis is essential for the maintenance and safety of the device as it is an important appliance used in various major sectors. Fault diagnosis at the proper time can reduce maintenance costs and save energy. This article uses a Simulink model based on mathematical equations to analyze the effects of parameter estimation of three-phase induction motor-based centrifugal pumps in inter-turn fault conditions. The inter-turn fault causes a massive in, a massive increase in current, which severely affects the parameters of both motor and pump. These have been analyzed by simulation through the Matlab Simulink model. Later, the results are verified by a hardware in loop (HIL) based simulator. In this paper, machine learning (ML) based artificial neural network (ANN) and ANFIS (ANN and Fuzzy) models have been applied for fault detection. ANN and ANFIS-based models provide a satisfactory level of accuracy. These models provide accurate training and testing results. Based on root mean square error (RMSE), R2, prediction accuracy, and mean validation value, these models are compared to find out which is more suitable for this experiment. Various supervised algorithms are compared with ANN, ANFIS, and lastly, found which is the most suitable for this experiment.

Harmonics Analysis of Air-Gap Magnetic Field of Induction Motors With Stator Inter-Turn Fault

The space–time distribution characteristics of the air-gap magnetic field (AGMF) determine the electromagnetic performance of the motor. This article studies the influence of the inter-turn fault (ITF) on space and time harmonics of the AGMF, and the relevant fault characteristics are obtained through analysis and experiments. First, a theoretical analysis is deduced to compare the components of AGMF before and after the fault, and all space and time harmonics are obtained. Then, according to the parameters of the prototype, the distortion and components of space and time harmonics are analyzed. Also, the influence of rotor slot harmonics is considered to analyze the variations of time harmonics. Finally, a detection wire method that can directly measure the AGMF is proposed, and the spectrum results of the measured signals prove the correctness of the research in this article. A fault feature can be extracted based on the frequency band analysis, and it is not affected by load variations. The results obtained through analytical analysis, simulation, and experiment further improve the AGMF research of induction motors with ITF.