Inter-turn Short Circuit Fault Research Articles

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.

Statistical Machine Learning Strategy and Data Fusion for Detecting Incipient ITSC Faults in IM

The new technological developments have allowed the evolution of the industrial process to this new concept called Industry 4.0, which integrates power machines, robotics, smart sensors, communication systems, and the Internet of Things to have more reliable automation systems. However, electrical rotating machines like the Induction Motor (IM) are still widely used in several industrial applications because of their robust elements, high efficiency, and versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions; hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs, and its appearance is due to electrical stresses leading to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology capable of performing the assessment and automatic detection of incipient electric faults like ITSC in IMs; the proposed method is supported through the processing of different physical magnitudes such as vibration, stator currents and magnetic stray-flux and their fusion of information. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as their fusion following a feature-level fusion approach and their reduction through the Linear discriminant Analysis technique. Furthermore, the fusion and reduction of information from different physical magnitudes lead to performing automatic fault detection and identification by a simple Neural-Network (NN) structure since all considered conditions can be represented in a 2D plane. The proposed method is evaluated under a complete set of experimental data, and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) can lead to achieving a global classification ratio of up to 99.4% during the detection of ITSC in IMs and an improvement higher than 30% in comparison with classical approaches that consider the analysis of a unique physical magnitude. Additionally, the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.

Diagnosis and Fault-Tolerant Control of Permanent Magnet Synchronous Motors With Interturn Short-Circuit Fault

In this brief, a hidden Markov model (HMM) is used to diagnose the severity of permanent magnet synchronous motors (PMSMs) with interturn short-circuit (SC) fault. The fault diagnosis result in this brief is an extension of similar result for dc motors. When the fault severity allows for continuing operation of the motor, a bisecting search method is proposed to reduce torque ripples while maintaining maximum torque capability. The advantages of the new fault-tolerant control (FTC) method over other existing methods in the literature are analyzed. Torque ripples are significantly reduced compared with the existing FTCs in the literature. The assumption of SC resistance equal to zero made in another paper was eliminated.

Estimation of Position and Shorted Turns Percentage of an Inter-Turn Short Circuit in Interior Permanent Magnet Synchronous Machines Based on a Current Observer and Stationary Reference Frame Tracking

This paper presents a method to detect and diagnose an inter-turn short circuit in interior permanent magnet synchronous machines. The proposed method detects the inter-turn short circuit fault using a current observer to estimate a disturbance related to the fault, defined as a fault index current. The fault index current estimates in the rotor reference frame have information about the location and the percentage of shorted turns. A novel stationary frame tracking technique is proposed to simultaneously determine the fault index current in the stationary frame and the fault position. Look-up tables are implemented to correlate the stationary frame fault index current magnitude to the percentage of shorted turns, providing an estimate of the percentage of shorted turns. Simulation and experimental results are shown throughout the paper to analyze and validate the proposed technique.

Transient thermal models of induction machines under Inter‐turn short‐circuit fault conditions

AbstractInduction machines are the working horses in a lot of industries. Inter‐turn short‐circuit (ITSC) fault is one of the most common failure modes taking place in them. It can generate large fault currents that lead to a local temperature rise in the faulty stator slot, which deteriorates the working performance of the machine or even cascades to a complete machine breakdown. This article focuses on developing transient thermal models for an induction machine under ITSC faults. The aim of these thermal models is to understand the thermal behaviour of the induction machine at different ITSC fault propagation stages. The first thermal model is based on finite element method (FEM) simulation that includes a physics‐based model for the thermal contact resistance. The second thermal model is a lumped parameter thermal network that models the stator as inter‐connected slot‐number parts. They are both capable of modelling the characteristic of non‐uniform temperature along the circumferential direction for the induction machine operating under ITSC fault conditions. After area‐weighted averaging the results of the FEM simulation, it is found they are quantitatively consistent, with an average relative error magnitude of 5%, as well.

Stator ITSC Fault Diagnosis for EMU Induction Traction Motor Based on Goertzel Algorithm and Random Forest

The stator winding insulation system is the most critical and weakest part of the EMU’s (electric multiple unit’s) traction motor. The effective diagnosis for stator ITSC (inter-turn short-circuit) faults can prevent a fault from expanding into phase-to-phase or ground short-circuits. The TCU (traction control unit) controls the traction inverter to output SPWM (sine pulse width modulation) excitation voltage when the traction motor is at a standstill. Three ITSC fault diagnostic conditions are based on different IGBTs’ control logics. The Goertzel algorithm is used to calculate the fundamental current amplitude difference Δi and phase angle difference Δθ of equivalent parallel windings under the three diagnostic conditions. The six parameters under the three diagnostic conditions are used as features to establish an ITSC fault diagnostic model based on the random forest. The proposed method was validated using a simulation experimental platform for the ITSC fault diagnosis of EMU traction motors. The experimental results indicate that the current amplitude features Δi and phase angle features Δθ change obviously with an increase in the ITSC fault extent if the ITSC fault occurs at the equivalent parallel windings. The accuracy of the ITSC fault diagnosis model based on the random forest for ITSC fault detection and location, both in train and test samples, is 100%.

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.

Modelling and Analysis of Inter-Turn Short-Circuit Faults for Large-Power SPM Wind Generators

This paper proposes a general analytical model for large-power surface-mounted permanent magnet (SPM) wind generators under inter-turn short-circuit (ITSC) faults. In the model, branch currents rather than phase currents are used as state variables to describe the electromagnetic behavior of the faulty machine. In addition, it is found that the multiphase Clarke transformation can be used to simplify the proposed fault model with the inductances calculated analytically or numerically using finite element analysis. With the latter, both linear and nonlinear inductances can be obtained, and the non-linear inductances are used for the fault modelling of large power rating machines due to larger electrical loading and heavier magnetic saturation. With the developed fault model, studies of scaling effects (different power ratings such as 3 kW, 500 kW and 3 MW) and the influence of fault location on the electromagnetic performance of SPM generators with series-parallel coil connections have been carried out. The simulation results show that large-power SPM wind generators are vulnerable to ITSC faults when a relatively small number of turns are short-circuited and a single-turn short-circuit fault at the top of the slot is found to be the worst case.

Online Stator Inter-Turn Short Circuit Estimation and Fault Management in Permanent Magnet Motors

This paper presents an online short circuit current amplitude estimation solution for permanent magnet synchronous motors (PMSM) with Inter-Turn Short Circuit (ITSC) fault in their stator winding. Short circuit current estimation amplitude is one of the most critical parameters while making critical decisions for the post-fault operations. Once the fault is detected, it is essential to control it to keep amplitude below rated current to prevent further winding degradation due to overheating. In this paper, a simplified ITSC fault model is developed for permanent magnet motors and integrated into the drive control for all permanent magnet motors. Based on the estimated amplitude of short circuit current, the speed reference is adjusted by an iterative scheme to bring short circuit current amplitude to a safe value. The adjusted speed reference defines the motor's Safe Operating Area (SOA) under ITSC fault. To test the efficacy and validity of the proposed method, experiments are conducted on lab prototype motors that have taps introduced in their stator windings to mimic ITSC faults.

Incipient Interturn Short-Circuit Fault Diagnosis of Permanent Magnet Synchronous Motors Based on the Data-Driven Digital Twin Model

As the most common fault of permanent magnet synchronous motor, inter-turn short circuit fault has great harm and develops rapidly. Once it is not diagnosed in time, it will bring secondary damage to the motor system. In the process of early fault diagnosis, the harmonic of the motor often increases the difficulty of fault feature extraction. In order to improve the reliability of the system, a method for early inter-turn short circuit diagnosis of permanent magnet synchronous motors based on data-driven digital twin models is proposed in this paper. Firstly, the three-phase current residuals of the permanent magnet synchronous motor under inter-turn short circuit fault are analyzed theoretically. Secondly, the digital twin model of the target motor in healthy states is established through NARX(Nonlinear Auto-Regressive model with Exogenous Inputs) network. Finally, the early short circuit diagnosis is completed through the analysis of the three-phase current residual. The method proposed in this paper does not need fault data and can complete the diagnosis of minor faults under the condition of large harmonic and certain inherent asymmetry. It has high sensitivity and obvious fault characteristics. Theoretical derivation and experiments verify the effectiveness of the proposed method.

Modelling and Analysis of Inter-Turn Short-Circuit Fault of PM Machines With Parallel-Connected Coils

This paper presents a general analytical fault model in a compact matrix form for surface-mounted permanent magnet (SPM) machines with parallel-connected coils, which is useful to study the machine performance under the inter-turn short circuit (ITSC) fault. To simplify the fault model, the multiphase Clarke transformation has been proposed. In the model, the branch currents rather than the phase currents are employed as state variables to describe machine behaviours under fault. Additionally, self- and mutual-inductances are obtained by winding function approach (WFA) plus slot permeance method. The proposed analytical fault model is applied to a 3 kW 96-slot 32-pole SPM machine and validated by time-stepping FE simulations. A small scale 12-slot 4-pole SPM machine prototype has also been built to further validate the accuracy of the proposed fault model.

Online Diagnosis and Discrimination of Stator Faults in Six-Phase Induction Motors Based on Voltage Symmetrical Components

In recent years, multiphase machines have been presented as a good alternative to the classical three-phase machines, mainly due to their ability to operate smoothly under fault conditions. Due to multiple stator phase arrangements, existing fault diagnostic (FD) methods are not sufficiently universal and cannot be applied to the multiple configurations of multiphase machines. In this regard, the current research proposes a universal and effective online diagnostics technique based on the computation and monitoring of a relevant severity factor, which is defined as the ratio of the zero, negative and positive voltage symmetrical components. The Short Time Least Square Prony’s (STLSP) approach is used to implement this method online in LabVIEW environment. In addition to the wide applicability of the method, the obviation of any motor parameter estimation and limited requirements regarding variables measurements make the proposed approach extremely advantageous for motors with high number of phases. This article addresses at stator faults in symmetrical six-phase induction machines (6PIM). The proposed method’s experimental results on six-phase motor drive platforms demonstrate the excellent generalization capability of the proposed method, as well as high accuracy and robustness, along with the ability to accurately distinguish between the occurrence of Inter-Turn Short Circuit (ITSC) faults and the presence of Unbalance Supply Voltage (USV) conditions. A 6PIM is put through its paces in both healthy and faulty conditions. The obtained results demonstrate the effectiveness of the proposed method for diagnosing the occurrence of ITSC faults and USV operating conditions with high reliability, fastness and high precision.

A Novel Indicator-Based Online Diagnostics Technique of Interturn Short-Circuit Faults in Synchronous Reluctance Machines

In recent years, the adoption of Synchronous Reluctance Machine (SynRM) drives for safety-critical equipment has witnessed notorious growth. The difficulties inherent to monitoring and fault diagnostics of SynRM drives are highly prominent. Inter-turn short-circuit faults (ITSCFs) are among the failure modes which are most critical and, simultaneously, more challenging to identify, in all types of motor drives. Still, the effective diagnostic task becomes even more challenging in SynRM drives, because of the harmonic pollution and intricated control configuration. To overcome the aforementioned challenges, the current research proposes a universal and effective online diagnostics technique, based on the computation and monitoring of a relevant severity factor, defined as the ratio of the zero, negative and positive voltage symmetrical components. The Short Time Least Square Prony’s (STLSP) approach is used to implement this method online in LabVIEW environment. In addition to the wide applicability of the method, the obviation of any motor parameter’s estimation and reduced requirements regarding variables’ measurements make the proposed approach extremely advantageous for motor drives with complex control architecture and operating conditions. The experimental results demonstrate the excellent generalization capability of the proposed method, as well as high accuracy and robustness. To confirm the claims, the proposed technique is implemented on a 2.2 kW SynRM drive, tested in both healthy and faulty conditions.

An Improved High-Frequency Voltage Injection Method for Interturn Short-Circuit Fault Detection in PMSMs

The inter-turn short-circuit fault (ITSCF) is a common incipient fault for the permanent magnet synchronous machine (PMSM) drive system. In recent years, the high-resolution ITSCF detection method that combines the high frequency (HF) signal injection and zero-sequence voltage measurement has been put forward. However, for the traditional HF signal injection technique, nonnegligible voltage distortion may occur when the injection frequency is close to the PWM carrier-frequency. In addition, the injected voltage varies with the operating conditions of the PMSM. These drawbacks of the traditional HF signal injection technique greatly limit the ITSCF detection performance using HF signal injection. To further improve the ITSCF detection performance, an improved HF injection scheme is proposed in this paper. In the proposed method, the six active voltage vectors are injected into the PMSM in certain orders to realize a rotating voltage injection, and the injection frequency is up to five-sixth of the PWM carrier-frequency. By employing the proposed injection method, the ITSCF detection sensitivity can be much improved. And the ITSCF detection performance is not affected by the operation conditions of the PMSMs. Finally, the simulations and experiments are carried out to verify the proposed method.

Fault detection and fault tole rant control of interturn short circuit of double redundancy motor

This paper presents the robust detection of interturn short circuit fault of double redundant permanent magnet synchronous motor. In the static coordinate system and double dq-axis system, the mathematical model of the fault state is established. Further, the residual signal of the dq-axis current caused by the turn-to-turn short circuit fault is predicted using the Luenberger observer. Next, the fault marker and the fault phase marker are obtained by processing the current residual signal. Finally, the processor performs the excision of the faulty phases and hardware reconfiguration, thus allowing fault-tolerant operation of the motor. The simulation results show the effectiveness of the detection and fault tolerance of double redundant permanent magnet synchronous motor.

Robust Novel Indicator To Distinguish between an Inter-Turn Short Circuit Fault and Load Unbalance in PMSG

In the aim to increase the reliability of the permanent magnet synchronous generator (PMSG), this paper proposes an efficient symmetrical component-based method to detect and accurately diagnose an incipient inter-turn short circuit (ITSC) fault in the PMSG without any confusion with a load unbalance. In fact, the existence of a negative sequence voltage component in the PMSG may be due to ITSC faults or to load unbalance, which creates confusion and affects the accuracy of the fault detection. To address this issue, this paper proposes a novel robust indicator capable of distinguishing without any ambiguity, an ITSC fault from a load unbalance in the machine. Hence, the objective of this paper is to demonstrate the behavior and the effectiveness of the novel indicator able to discriminate between the two operating modes, through original thorough analytical and simulation studies, validated experimentally on a real PMSG.

Modeling of Symmetrical Six-Phase Induction Machines Under Stator Faults

This paper addresses the occurrence of stator faults in six-phase induction machines, with a symmetrical winding, operating under both balanced and unbalanced supply voltage conditions. A dynamic model of the motor was developed in Matlab/Simulink environment, taking into consideration the occurrence of inter-turn short circuit faults. To validate the proposed approach, simulation results, covering the full range of operation and different fault severity levels, are compared with the motor experimental performance. The voltages and currents obtained from the simulation present a good agreement with the corresponding experimental results, demonstrating the accuracy of the proposed model

Electromagnetic Vibration Characteristics of Inter-Turn Short Circuits in High Frequency Transformer

As a common fault of transformer winding, inter-turn short circuits cause severe consequences, such as excessive current and serious deformation of winding. Aiming to solve the problem of inter-turn short circuit at the end-winding and middle-winding of high frequency transformers (HFT), this paper considers the electromagnetic vibration characteristics of inter-turn short circuits (interleaved winding and continuous winding) at different positions, and the HFT is established by the multi-physical field coupling principle. Coupling equations for the inter-turn short circuit, as well as electromagnetic force and sound pressure level, are established to characterize the vibration noise mechanism of inter-turn short circuits. Furthermore, the HFT equivalent model is simulated in 3D finite element method (FEM) to emulate the real transformer operation and investigate the impact of interleaved winding and continuous winding under inter-turn short circuit faults. The short-circuit current and axial flux leakage, as well as the harmonic response of vibration acceleration and sound pressure level distribution, are obtained when inter-turn short circuits occur at different positions. Finally, the results show that the electromagnetic effect of the inter-turn short circuit in end-winding is worse than it is in middle-winding. Advantages in resisting impulse current make interleaved winding superior to continuous winding in terms of vibration and noise.

PWM Harmonic-Current-Based Interturn Short-Circuit Fault Diagnosis for the Aerospace FTPMSM System Even in the Fault-Tolerant Operation Condition

This paper proposes a new PWM harmonic current based diagnostic method for the interturn short-circuit fault (ITSCF) of fault tolerant permanent magnet synchronous motor (FTPMSM) drives, which is able to detect and locate the fault phase for the system even in fault tolerant operation (FTO) condition. The ITSCF model for the H-bridge drive based FTPMSM system is firstly proposed, while the high-frequency (HF) harmonic current component and impedance are analyzed both in normal and ITSCF conditions. The PWM harmonic current based fault diagnostic approach is then proposed to confirm the fault phase, which consists of the band-pass filter, the periodic root mean square (RMS) calculator and the fault identification. The resulting fault diagnostic method has simple structure, fast fault detection, good robustness and low computational burden, which can achieve the ITSCF for the FTPMSM system both under normal and FTO conditions. The effectiveness of the ITSCF diagnostic method is verified via the six-phase FTPMSM experimental platform, which can provide a guideline for the ITSCF mitigation and FTO of the FTPMSM system.