Inter-turn Fault Detection Research Articles

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

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

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

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

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

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

Inter-turn fault detection in PM synchronous motor by neuro-fuzzy technique

In this paper, a method for detecting the stator internal coil fault detection for a permanent magnet synchronous motor (PMSM) using the ANFIS algorithm is proposed and described. At first, the dynamic model of the synchronous motor along with its certain fault will be introduced. Since fault detection in these engines is very important and has a high value, different methods have been proposed for detecting stator deflection in electric machines. To determine the fault percentage in the permanent magnet synchronous motor, a neuro-fuzzy adaptive inference system is used to identify the fault. The advantages of the proposed algorithm are the ability to detect faults with different domains. It is flexible enough to be used for offline and online identification. For this reason, we have used neuro-comparative learning techniques in fuzzy logic in this paper. The inputs of the proposed algorithm are two PMSM current and torque signals in normal and faulty conditions. In the proposed algorithm, the membership function structure was created with the fuzzy C-means clustering method. The simulation results show that the proposed algorithm can accurately determine where and with what speed the fault occurs.

A Simple and Efficient Current-Based Method for Interturn Fault Detection in BLDC Motors

Fault diagnosis of brushless direct current (BLDC) motors has recently gained much attention. Interturn faults in BLDC motor distort three-phase currents waveforms, leading to an asymmetrical condition. In this article, we introduce a simple and efficient method to detect interturn faults based on one modal current and four different simple indices. The modal current is derived by proper linear mixing of measured three-phase currents. Following the initial processing of the modal current, three main indices, including moving mean, variance, and signal energy, are obtained in parallel. Also, an auxiliary correlation-based index is suggested to enhance the method for discrimination of faulty conditions from healthy ones. The fault detection is made by passing at least two main indices (out of three indices), and also an auxiliary index from a predefined threshold. The proposed technique is evaluated under different loads, speeds, and fault severities in two different datasets: data from a simulated motor in Maxwell finite element package, and a real four-pole motor. Moreover, the method is compared with other methods from different aspects. The results confirm a high accuracy and quick fault detection in the proposed approach.

High-Frequency Voltage Injection Based Stator Interturn Fault Detection in Permanent Magnet Machines

An interturn short-circuit fault in the stator winding of an electric machine, denoted as turn fault, has been recognized as one of the most severe faults in permanent magnet machines, which require a swift and reliable detection, in order to implement appropriate mitigations. The asymmetry brought by a turn fault is widely used for fault detection. However, similar features also emerge in a less severe high-resistance connection (HRC) fault, which may lead to an incorrect fault identification. In this article, a more exclusive turn fault detection method with the ability to differentiate from the HRC fault is proposed. It injects high-frequency square-wave voltage signals and makes use of the difference in the high-frequency impedance under the two fault conditions. The sensitivity to the HRC fault is largely reduced. The proposed turn fault indicator is independent of the operating conditions and robust with respect to state transients. This method is validated in a fault-tolerant permanent magnet assisted synchronous reluctance machine drive.

Reliable Detection of Stator Interturn Faults of Very Low Severity Level in Induction Motors

The interturn faults are one of the most (if not the most) challenging electrical machine failures to detect online and at incipient severity stages. Past works and experience have shown that this specific fault type will lead to a ground fault and consequently a catastrophic failure of the machine in a very small amount of time. Past works have proposed techniques and methodologies to confront this dangerous fault. However, a common feature in most past efforts is the high level of severity, limited by external resistors to avoid machine breakdowns. Scenarios like the above are not of much use in industry because they lead to the development of diagnostic techniques insensitive to the real fault severity levels that are noncatastrophic and that are by their nature very low. This is the motivation behind this article, which challenges the existing background in this field and offers a reliable solution, which may be adopted in industry. The article studies induction motors with incipient interturn fault severity with many well-known techniques. The experimental results prove many methods unreliable and insensitive to low level interturn fault detection. Finally, the authors propose a novel method that relies on the monitoring of the stray flux at three positions of the machine.

Method of inter‐turn fault detection for next‐generation smart transformers based on deep learning algorithm

In this study, an inter-turn fault diagnosis method is proposed based on deep learning algorithm. 12-channel data is obtained in MATLAB/Simulink as the time-domain monitoring signals and labelled with 16 different fault tags, including both primary and secondary voltage and current waveforms. An auto-encoder is presented to classify the fault type of the abundant and comprehensive fault waveforms. The overall waveforms compose a two-dimension data matrix and the auto-encoder is trained to extract the features in the multi-channel waveforms. The selected features are convoluted with the original data, generating a one-dimensional vector as the input to the softmax classifier. Variables such as type, activation function and depth of auto-encoder, sparsity of sparse auto-encoder, number of features and pooling strategies are studied, which gives an intuitive process to train a proper learning model. The overall recognition accuracy reaches 99.5%. Signal characteristics such as channel selection, time span of the input signal and signal sampling frequency are studied to find the best solution for the inter-turn fault detection of the three-phase transformer. The proposed method under deep learning framework increases the accuracy and robustness in transformer fault diagnosis, indicating its potential and prospect in the next-generation smart transformers.

Improved ZSVC‐based fault detection technique for incipient stage inter‐turn fault in PMSM

Fault detection plays an important role in providing reliable operation for permanent-magnet synchronous machine (PMSM). The inter-turn fault is one of the most common faults for the PMSM. Hence, this study focuses on the incipient stage inter-turn fault detection. An improved zero-sequence voltage component-based (ZSVC) inter-turn fault detection method is proposed. In the proposed method, discrete wavelet transform is first applied to remove the noise and harmonic components in the ZSVC for highlighting the fault characteristic component. Then, fast Fourier transform is used to analyse the obtained signal for the inter-turn fault detection. In addition, to show the performance of the proposed method, the commonly used fault detection based on stator current is studied. The effectiveness of the proposed fault diagnosis method is validated by the simulation and experimental results, showing that the proposed method has good performance for the incipient stage inter-turn fault diagnosis.

Inter-Turn Short-Circuit Fault Detection Approach for Permanent Magnet Synchronous Machines Through Stray Magnetic Field Sensing

Fast online detection of inter-turn short-circuit faults for permanent magnet synchronous machines (PMSMs) is a challenging task. Previous attempts based on phase currents or back EMF monitoring lack the capability to accurately locate the inter-turn short-circuit faults in stator windings. This paper proposed a new approach to online inter-turn short-circuit fault detection for PMSMs by means of sensing the external stray magnetic field outside the motor stator yoke. The stray magnetic field provides the information about the phase currents as well as the status of stator windings and thus, the location of inter-turn short-circuit fault can be detected accordingly. In this paper, the inter-turn short-circuit model and the stray magnetic flux model for PMSMs were theoretically analyzed. Both FEM simulations and experiments have been presented to validate the effectiveness of the proposed approach. A sensor array composed of 24 sensitive tunneling magnetoresistive (TMR) sensor units was employed to measure the stray magnetic field distribution outside the PMSM stator yoke. The experimental results show that both the location and severity of the inter-turn short-circuit fault in PMSMs were effectively detected using the proposed approach.

Inter-Turn Fault Detection of Dry-Type Transformers Using Core-Leakage Fluxes

In this paper, a new technique for low-severity inter-turn fault detection under online operating conditions of dry-type transformers is proposed. The proposed methodology requires just two inexpensive flux sensors and is based on the fundamental component of the core-leakage fluxes at the transformer's outer limbs. Theoretical analysis using the proposed analytical models and experimental validation on a 10-kVA experimental prototype transformer are carried out to evaluate the performance of the proposed methodology under various fault severities. The real-time fault-detection capability of the proposed technique is also evaluated using a fabricated prototype health monitoring device, which reveals that as low as 2-turns bolted fault out of 150 turns can be detected within 3 fundamental cycles. The methodology is immune to loading and supply voltage unbalance conditions and on par with the classical differential negative sequence current based technique with an additional advantage of 78% reduction in the total number of sensors required for a three-phase three-winding transformer.

Detection of Inter-Turn Faults in Multi-Phase Ferrite-PM Assisted Synchronous Reluctance Machines

Inter-turn winding faults in five-phase ferrite-permanent magnet-assisted synchronous reluctance motors (fPMa-SynRMs) can lead to catastrophic consequences if not detected in a timely manner, since they can quickly progress into more severe short-circuit faults, such as coil-to-coil, phase-to-ground or phase-to-phase faults. This paper analyzes the feasibility of detecting such harmful faults in their early stage, with only one short-circuited turn, since there is a lack of works related to this topic in multi-phase fPMa-SynRMs. Two methods are tested for this purpose, the analysis of the spectral content of the zero-sequence voltage component (ZSVC) and the analysis of the stator current spectra, also known as motor current signature analysis (MCSA), which is a well-known fault diagnosis method. This paper compares the performance and sensitivity of both methods under different operating conditions. It is proven that inter-turn faults can be detected in the early stage, with the ZSVC providing more sensitivity than the MCSA method. It is also proven that the working conditions have little effect on the sensitivity of both methods. To conclude, this paper proposes two inter-turn fault indicators and the threshold values to detect such faults in the early stage, which are calculated from the spectral information of the ZSVC and the line currents.

A Comprehensive Monitoring System for Online Fault Diagnosis and Aging Detection of Non-Isolated DC–DC Converters’ Components

Online monitoring is an inevitable necessity for dc–dc converters in industrial applications to achieve a fault-tolerant structure, which ensures reliable operation and prevents catastrophic failures. In this paper, a comprehensive monitoring system is proposed for non-isolated dc–dc converters. The proposed approach is mainly focused on monitoring the most prone to failure components of these converters including switching devices and capacitor. Using the proposed monitoring system, switch/diode open circuit and short circuit faults are detected in less than one switching cycle. Furthermore, online monitoring of the capacitor equivalent series resistance and switch on-state voltage as effective precursors of aging is also carried out. Finally, an inter-turn fault detection technique is presented for the converter inductor. The mentioned monitoring tasks are accomplished using only two electrical sensors (for capturing voltages across the converter diode and inductor) and two temperature sensors. The presented monitoring approach is simple, cost-effective, easy to implement, and applicable for all non-isolated dc–dc converters including buck, boost, and buck-boost converters. Performance of the monitoring system is evaluated by considering different scenarios in the experiments carried out on a laboratory prototype. The results confirm the effectiveness of the proposed system for fault diagnosis and aging monitoring in non-isolated dc–dc converters.

Detection of the Stator Winding Inter-Turn Faults in Asynchronous and Synchronous Machines Through the Correlation Between Harmonics of the Voltage of Two Magnetic Flux Sensors

This paper presents a statistical methodology for detection of inter-turn short-circuit fault in asynchronous and synchronous machines. This methodology uses a correlation coefficient obtained from external magnetic field measured in the machine vicinity. It is a noninvasive method which follows up the signals of two external flux sensors located symmetrically around the machine axis (180° spatially shifted). The principle is based on the calculation of the Pearson correlation coefficient between two signals delivered by two sensors S1 and S2 when the machine operates at different load conditions, which allows us to detect incipient faults in electrical induction and synchronous machines with a high probability of detection. Experimental tests are realized using two specific rewound machines to create inter-turn short-circuit faults with different severity levels.

Modeling and Diagnosis of the Inter-Turn Short Circuit Fault for the Sensorless Input-Output Linearization Control of the PMSM

The purpose of this paper is the inter-turn short circuit fault Modeling and detection for the sensorless input-output linearization control of the permanent magnet synchronous motor (PMSM) based on the Extended Kalman Filter observer (EKF). The fault detection procedures are based through the estimation of the stator resistance variation by the Extended Kalman Filter observer and the Fast Fourier Transformer (FFT) for the stationary state, and the Discrete Wavelet Transform (DWT) analysis of the electrical characteristics of the PMSM, for the non-stationary state. However, the FFT spectral analysis and the DWT is a useful solution to ensure that the variation of the stator resistance estimation is caused by the inter-turn short circuit fault. The effectiveness of the sensorless control and the fault detection techniques are presented in a simulation in MATLAB/Simulink environment.

A Novel Approach for Sensitive Inter-turn Fault Detection in Induction Motor Under Various Operating Conditions

Induction motors are subjected to thermal, electrical and mechanical stresses during continuous operation. If any of these stresses become excess than normal condition, it is a symptom of commencement of fault in the induction motor. If these faults are not detected at an incipient stage, it may result in the failure of the motor. Turn-to-turn short-circuit faults are the major causes for the stator winding insulation failure. In the proposed work, a novel approach is suggested to detect these faults in the induction motor. In this approach, the discrete wavelet transform (DWT) is performed on the Park’s vector modulus of current signals. The accuracy of DWT can be enhanced by selecting the proper wavelet type and its order along with levels of decomposition. With this context, an attempt is made to investigate the best-suited mother wavelet by testing various orthogonal wavelet functions on simulated signal and justified that the db44 is to be the best suitable wavelet for the detection of an inter-turn fault in the induction motor. The current signals are obtained experimentally under operating conditions with balanced supply voltages, unbalanced supply voltages, load slip variation and gradual variation in load condition and analyzed. The obtained results show the sensitivity and robustness of the proposed approach. The fault severity factor is also suggested for evaluating the severity of fault in the induction motor.

Validity of frequency response analysis (FRA) for diagnosing large salient poles of synchronouos machines

Frequency Response Analysis (FRA) is a technique under research for its application in rotary electrical machines. Even though FRA has been used for around 40 years in power transformers diagnosis, its application is still brand new in synchronous machines. The FRA technique is based on the comparison to a previous reference test that is done in healthy conditions. This article presents numerous experimental factory tests in a pole of a 40-MVA synchronous machine and onsite tests upon a 106-MVA synchronous generator in a hydro power plant where the FRA test was performed on a pole inside and outside the machine. Furthermore, simulations were executed in order to calculate the impact of the stator core on the FRA reference test. The main contribution of this article is demonstrating that the stator core has a significant influence on poles FRA results. Therefore, FRA tests done inside the machine cannot be compared to FRA tests performed outside the machine or vice versa, in order to avoid fault positives. FRA can be considered a suitable technique for the ground and interturn faults detection in poles of large synchronous machines.

Stator Winding Fault Thermal Signature Monitoring and Analysis by In Situ FBG Sensors

Winding short circuit faults are recognized as one of most frequent electric machine failure modes. Effective on-line diagnosis of these is vital, but remains a challenging task, in particular, at incipient fault stage. This paper reports a novel technique for on-line detection of incipient stator short circuit faults in random wound electrical machines based on in situ monitoring of windings thermal signature using electrically nonconductive and electromagnetic interference immune fiber-Bragg grating (FBG) temperature sensors. The presented method employs distributed thermal monitoring, based on the FBG multiplexing feature, in a variety of points within windings, in proximity to thermal hot spots of interest that arise from fault. The ability of the proposed method to enable fault diagnosis through identification of fault-induced localized thermal excitation is validated in steady-state and transient operating conditions on a purpose built inverter driven induction machine test facility. The results demonstrate the capability of unambiguous detection of inter-turn faults, including a single shorted turn. Furthermore, the winding thermal and electrical characteristics at the onset of inter-turn fault are examined and correlated, enabling better understanding of fault diagnostic requirements.

Fault Detection of Stator Inter-Turn Short-Circuit in PMSM on Stator Current and Vibration Signal

The stator inter-turn short circuit fault is one of the most common and key faults in permanent magnet synchronous motor (PMSM). This paper introduces a time–frequency method for inter-turn fault detection in stator winding of PMSM using improved wavelet packet transform. Both stator current signal and vibration signal are used for the detection of short circuit faults. Two different experimental data from a three-phase PMSM were processed and analyzed by this time–frequency method in LabVIEW. The feasibility of this approach is shown by the experimental test.

Electromagnetic Analysis and Electrical Signature-Based Detection of Rotor Inter-Turn Faults in Salient-Pole Synchronous Machine

In this paper, rotor inter-turn faults in salient-pole synchronous machines are investigated using finite-element analysis. The machine under study is a 22 MVA eight-pole hydropower generator with fractional-slot windings. Simulated faulty cases include 1, 2, 5, 10, and 20 short-circuited turns (out of 58 turns) in one rotor pole. In the electromagnetic analysis, the effect of faults on airgap flux density and radial forces is studied. Furthermore, this paper investigates online detection of inter-turn faults using spectral analysis of stator voltage or current is studied. The amplitude of fault-related harmonics is investigated at various fault severities, as well as at no-load and full-load operations. This paper also discusses the effects of damper bars, an influence of parallel circuits in the stator windings, and influence of the pole pair number. In addition, a comparison between fault-related harmonics in the case of dynamic eccentricity and rotor inter-turn faults is presented.