Stator Inter-turn Short Circuit Fault Research Articles

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.

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.

Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

A novel approach by introducing a statistical parameter to estimate the severity of incipient stator inter-turn short circuit (ITSC) faults in induction motors (IMs) is proposed. Determining the incipient ITSC fault and its severity is challenging for several reasons. The stator currents in the healthy and faulty cases are highly similar during the primary stage of the fault. Moreover, the conventional statistical parameters resulting from the analysis of fault signals do not consistently show a systematic variation with respect to the increase in fault intensity. The objective of this study is the early detection of incipient ITSC faults. Furthermore, it aims to determine the percentage of shorted turns in the faulty phase, which acts as an indicator for severe damage to the stator winding. Modeling of the motor in healthy and defective cases is performed using the Clarke Concordia transform. A discrete wavelet transform is applied to the motor currents using a Daubechies-8 wavelet. The statistical parameters <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{1}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{2}$</tex> norms are computed for the detailed coefficients. These parameters are obtained under a variety of loads and defects to acquire the most accurate and generalized features related to the fault. Combining <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{1}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{2}$</tex> norms creates a novel statistical parameter with notable characteristics to achieve the research aim. An artificial neural network-based back propagation algorithm is employed as a classifier to implement the classification process. The classifier output defines the percentage of defective turns with a high level of accuracy. The competency of the adopted methodology is validated via simulations and experiments. The results confirm the merits of the proposed method, with a classification test correctness of 95.29%.

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.

Fault indicator based on stator current under early stage SISC in synchronous condensers

In this study, the stator current characteristics of synchronous condensers for stator interturn short-circuit (SISC) fault in the early stage are investigated using theoretical derivation, finite-element analysis (FEA) and experimental research. In contrast to previous studies, this study considers the real operation characteristics of synchronous condensers that need to change the exciting current to meet the reactive power demand of the power grid. Thus, a new SISC fault indicator (FI) based on the stator current is proposed in this study. The new FI can identify incipient SISC and suppress the interference of the exciting current variation. A customized synchronous machine and TTS-300-2 synchronous condenser are simulated using FEA to demonstrate the effectiveness of the FI. Furthermore, experimental tests are conducted in a customized synchronous machine to prove the practical significance of the studies.

Impact of stator interturn short circuit fault on shaft voltage in a synchronous generator

AbstractShaft voltage often exists in synchronous generators due to the assembly error or various faults after the long‐term performance. The article investigates the shaft voltage characteristic under the stator interturn short circuit (SISC) condition. Different from other studies, this article mainly considers the mapping relationship between the amplitude–frequency characteristics of the shaft voltage and the SISC degrees. The detailed shaft voltage expressions are first derived based on the magnetic flux density under the normal condition and SISC condition. Then the finite element analysis and the experiment is studied on a CS‐5 prototype synchronous generator with two poles in order to validate the theoretical formula. The result shows that there is no shaft voltage in the normal condition, while the shaft voltage is generated obviously under the SISC condition. Meanwhile, the frequency component of the shaft voltage is mainly composed of odd harmonics including the first, third and fifth harmonics.

Stochastic Event-Triggered Fault Detection and Isolation Based on Kalman Filter.

In this article, the robust fault detection and isolation (FDI) Kalman filter is extended based on stochastic event-triggered schedulers for discrete linear systems consisting of deterministic/stochastic unknown inputs with nonzero mean and colored measurement noise. In the proposed FDI method, first, a subspace of the main system that significantly attenuates disturbance effects is proposed. After that, the fusion method is proposed for dealing with the colored measurement-noise problem in designing the Kalman filter and preventing from leading to measurement noise with zero mean and zero covariance. The stochastic event-triggered schedulers are considered as Gaussian functions. Therefore, the Gaussian property of innovation sequence is preserved, and consequently, the recursive equation of the Kalman filter need not be extended based on approximation techniques. Finally, design parameters are chosen based on the convex optimization problem such that the lowest communication rate between the sensor node and FDI filter, and also the best FDI performance are achieved. The proposed FDI method is evaluated to detect and isolate stator interturn short circuit and broken rotor bars faults with unbalanced voltage as disturbance in three-phase induction motors.

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.

Induction machine analysis with extensive stator interturn fault conditions

This work investigates stator interturn short-circuit faults in induction machines. The goal is to examine possible fault signatures used in detection methods for interturn short-circuit faults. For this purpose, a faulted induction machine is analysed through a finite-element model. Great attention is given to the sequence components and harmonic content of the stator currents. Finite-element-based results are provided and discussed for an extensive set of fault conditions. The stator-current negative sequence and the rotor slot harmonics are dependent on the fault presence. An induction machine prototype with tapped windings is used for experimental tests where an extensive amount of fault conditions is implemented. The experimental tests validate the finite-element model and the fault signatures.

The Effect of Stator Inter-Turn Short-Circuit Fault on DFIG Performance Using FEM

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.

Research on Diagnosis and Prediction Method of Stator Interturn Short-Circuit Fault of Traction Motor

The traction motor (TM) is an essential part of the high-speed train, the health condition of which determines the quality and safety of the vehicle. Hence, this study proposed a novel approach to diagnosing and predicting the TM stator interturn short-circuit fault (SISCF). Based on the Park vector (PV) of the stator current, this method could overcome the interference of current sensor errors, null shift, and motor frequency fluctuations in the actual conditions. More specifically, Park’s transformation was used to obtain the PV of the stator current. Then, the PV was fitted to obtain the elliptical trajectory and its parameters from which the negative sequence component of the stator current could be calculated. Finally, the SISCF diagnosis and prediction method were realized by the magnitude and trend of the negative current as well as the inclination of the trajectory ellipse. Furthermore, the performance of the proposed method was validated by a simulation model and a series of experiments. The simulation results were consistent with the experimental results, supporting the validity and correctness of the method proposed in this study.

Drive Integrated Start-Up and Online ITSC Fault Monitoring Through Spatial Inductance Profiling

Fault diagnosis and condition monitoring of electric machines using readily available drive sensors are highly critical in safety-critical applications. This article presents an accurate method to detect stator interturn short-circuit (ITSC) fault using the spatial inductance profile, which can be implemented both at start-up (standstill) or in real time. For this purpose, ac motor drive spatially scans the motor at each start-up in milliseconds by injecting high-frequency signals to obtain spatial inductance profile, analyze the fault status, and locate faulty phase. Using the start-up inductance profiling as reference, online detection version is implanted successfully, which is essential for real-time condition monitoring. This approach is highly sensitive ITSC faults, agnostic to temperature variations, and takes inverter nonlinearities into account caused by dead time and voltage drop across the power devices. To demonstrate the efficacy and practicability of the proposed algorithm, it is experimentally verified on two PMSMs, interior permanent magnet synchronous motor (IPMSM) and an induction motor, all rewounded to mimic ITSC faults. It is shown that the algorithm can detect ITSC faults very accurately in the order of milliseconds even if the number of shorted turns is very low.

Inter-turn short-circuit fault diagnosis using robust adaptive parameter estimation

This paper presents a robust adaptive parameter estimation procedure, applied to stator inter-turn short-circuit (ITSC) fault detection in induction motors (IM). Using a fault state space model, it is proposed the application of an adaptive parameter estimation methodology, used to identify a parameter related to the failure severity. A linear parameter varying (LPV) robust H∞ filtering technique, obtained by linear matrix inequalities (LMI), is applied to estimate the unavailable states, being the proposed method robust to external disturbances, and also adequate to online applications. The proposed framework is validated through simulations and experimental tests — conducted in a modified IM that allows the emulation of ITSC faults. Tests are performed under several operational conditions, including different levels of fault severity, load torque and voltage unbalance, demonstrating the validity of the ITSC fault detection approach.

Fault Injection Model of Induction Motor for Stator Interturn Fault Diagnosis Research Based on HILS

Recently, in order to ensure the reliability and safety of trains, online condition monitoring and fault diagnosis of traction induction motors have become active issues in the area of rail transportation. The fault diagnosis algorithm can be developed and debugged in a real-time environment based on hardware-in-the-loop simulation (HILS). However, the dynamic space model of induction motors with stator interturn short-circuit faults faces the problem that the faulty state and the healthy state are not compatible, which is inconvenient for the HILS. In this paper, a fault injection model is proposed for the first time, which can realize the online switching between the healthy state and the faulty state of the motor. The feasibility and effectiveness of the proposed model are verified by simulation experiments the based on MATLAB/Simulink and dSPACE HILS platforms.

Reactive power characteristics and vibration properties under SISC in synchronous condensers

A stator interturn short-circuit (SISC) fault in synchronous condensers can bring about serious damage if the fault is not detected in time. At present, researches on SISC fault characteristics and diagnosis methods are still scarce because only in recent years are synchronous condensers adopted in power grids. Different from previous studies, this paper considers the practical operation condition of synchronous condensers that need to change exciting current frequently and work in either under-excitation or over-excitation according to the actual demand of power grids. Detailed reactive power and unbalanced magnetic pull (UMP) expressions under SISC are deduced firstly based on the analysis of the magnetic flux density variation. It can be seen that the reactive power increases under under-excitation running and decreases under over-excitation running after SISC. The second, fourth and sixth harmonics of stator UMP/vibration are enlarged under the SISC. Specifically, the sixth harmonic augments with the increase of exciting current in the over-excitation running, while the variation of the sixth harmonic with exciting current depends on parameters and fault degree of synchronous condensers under the under-excitation operation. Moreover, the two-dimensional finite-element analysis (FEA) and experimental tests are performed on a customized synchronous machine. In addition, the FEA of a TTS-300-2 synchronous condenser is accomplished in order to validate the proposed theoretical analysis further. The research conclusions of fault characteristics in this paper provide potential solutions for the monitoring and diagnosis of SISC in synchronous condensers.

Diagnosis of stator fault severity in induction motor based on discrete wavelet analysis

This research puts forward a novel approach by proposing mathematical equations to estimate the fault intensity index in induction motor with stator inter-turn short circuit (ITSC) fault. Determination of the failure index is significant to create appropriate mitigation methods, as well as to define a safe working area for the motor to prevent the subsequent damage to the stator winding. Discrete Wavelet Transform (DWT) is employed to analyze the obtained raw currents in the time domain. From the detailed coefficients, the statistical parameter maximum norm has been computed under a variety of loading conditions, in addition to several fault severities. The originality of this work is to present an efficient and robust method based on new mathematical equations to detect the ITSC fault at an early level and to precisely specify the number of shorted turns in the defective phase. The methodology provides notable results, reduces computational time consumption, and simplifies the estimation process remarkably. In order to validate the efficacy of the proposed method, several experiments are conducted on the machine. The hardware experimental outcomes demonstrate the practicability and competency of the methodology, with a high level of correctness.

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.

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.

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