Inter-turn Short Circuit Fault Research Articles

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.

Electromagnetic Design Characterization of a Dual Rotor Axial Flux Motor for Electric Aircraft

This article presents and evaluates a dual rotor axial flux permanent magnet motor for electric aircraft applications. Several features, including grain-oriented electrical steel, Halbach arrays, and wires with rectangular cross sections, are used to improve torque density and efficiency. A novel winding arrangement is used to mitigate interturn short-circuit faults. Rather than simply optimizing the motor by itself, this article evaluates the tradeoffs between motor performance and its interfaces with the drive, thermal management system (TMS), and mechanical structure. This information can be used along with similar analyses of these subsystems to select the design with the system-level optimal performance. This article uses finite-element simulations to characterize tradeoffs among active mass, efficiency, fundamental frequency, power factor, axial forces on the rotors, and cooling surface area. Several designs exceed 95% efficiency at takeoff with less than 8 kg of active mass. While high pole counts, a large outer radius, and short stator teeth tend to optimize the magnetic performance at takeoff, this can reduce cruise efficiency, reduce the surface area through which the TMS can extract heat, increase the fundamental frequency the drive must supply, increase the structural mass required to support the rotors, and introduce complexity to the manufacturing process. Further analysis for a selected design reveals that the power factor can be significantly improved with a minimal torque penalty via field weakening due to significant saturation in the stator teeth.

Incipient detection of stator inter‐turn short‐circuit faults in a Doubly‐Fed Induction Generator using deep learning

Wind turbines are increasingly expanding worldwide and Doubly-Fed Induction Generator (DFIG) is a key component of most of them. Stator winding fault is a major fault in this equipment and its incipient detection is of vital importance. However, there is a paucity of research in this field. In this study, a novel machine learning-based method is proposed for incipient detection of inter-turn short-circuit fault (ITF) in the DFIG stator based on the current signals of the stator. The proposed method makes use of state-of-the-art deep learning methods along with conventional signal processing tools and general machine learning techniques. More specifically, the incipient fault detection problem is regarded as a multi-class classification problem and a Long Short-Term Memory network, which is more appropriate for time-series data is utilised for inference. Furthermore, a variant of the celebrated Empirical mode Decomposition analysis tool is used to extract some well-known statistical features among which the most informative ones are selected using a new feature selection method. Our tests using experimental data in steady-state conditions show that the proposed method can accurately detect ITF fault at its initial stage when only one turn is shorted. Moreover, its performance is considerably higher than that of a variety of machine learning-based methods.

A novel online method for locating fault coil in direct‐drive permanent magnet synchronous motor with inter‐turn short‐circuit fault using search coil array

This study presents a fault coil localisation approach for a direct-drive permanent magnet synchronous motor (DDPMSM) with inter-turn short-circuit fault (ISF) using the search coil (SC) array. The SCs are wound around specific stator tooth of DDPMSM through the proposed SC array arrangement principle, which reduces the invasiveness of the fault coil localisation method. The SCs are classified as several search coil groups (SCGs), the differential between SC back electromotive forces (EMFs) in the same SCGs (δ1) and SC residual back EMF (δ2) are used as fault indicators for ISF localisation. The peak values extracted from δ1 and δ2 are the basis for diagnosing ISF. Hence, the proposed approach does not need a complicated signal processing algorithm, and it is less complex. First, the SC back EMF analytical model is developed to investigate the mapping relationship between the back EMF of each SC and fault coil location. Second, fault coil localisation indicators for ISF are analysed based on the proposed analytical model, and the exact ISF localisation approach is presented. Finally, two basic installation units and the arrangement principle of SC array in DDPMSM are proposed. Simulation and experimental results justify the effectiveness of the selected fault indicators and the feasibility of the proposed SC array for diagnosing ISF.

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.

Improved direct torque control on an induction machine with short circuit fault

Direct torque control (DTC) is one of the most widely used vector controls to improve the transient behavior of electrical and mechanical quantities in induction machines (IM). The inter-turn short circuit (ITSC) fault is one of the most common faults in this type of machines; this fault significantly degrades the expected performance of the DTC. For a certain short circuit rate, this type of control cannot be launched and therefore a diagnosis at zero speed is required before launching the control. In this paper, we propose the simulation of the behavior of the DTC of an IM when an ITSC fault appears. The simulation of this type of converter-machine association has given very interesting results in terms of stability, accuracy and the ripple of the mechanical quantities torque and speed. This paper presents an interesting possibility to improve the behavior of the DTC for a tolerable short-circuit rate less than 15%. For this value, the new performances of this controller-machine association show a significant improvement in stability (from unstable to stable), in speed ripple (from unstable to 1.17%) and in torque ripple (from unstable to 170%).

Backstepping Control of a Switched Reluctance Motor with Inter-Turn Short-Circuit

Improving the behavior of speed control in electric vehicles is currently a major challenge for researchers and engineers. For this study, we chose the context of a Switched Reluctance Motor (6/4 SRM) used in a hybrid Electric Vehicle with Extended Range (EREV). Speed regulation is an essential feature on long-distance trip. Speed regulators of backstepping type are very effective in this context given the non-linear nature of switched reluctance motors. The estimation of non-linear quantities, flux and inductance, uses Legendre polynomials. The control strategy uses four regulators, one for speed and three for stator currents. It is based on the Torque Sharing Function (TSF) and the Torque Inverse Model (TIM). Our simulation consists of studying the behavior of this type of control when an Inter-Turn Short-Circuit (ITSC) fault appears on one of the phases of the 6/4 SRM. In this paper, we are interested in temporal behavior of phasic currents and we will show the interest of these quantities as fault indicators allowing the real time diagnosis of this type of controller-machine.

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.

An Effective Federated Learning Verification Strategy and Its Applications for Fault Diagnosis in Industrial IoT Systems

Due to the diverse equipment and uneven load distribution in industrial environments, data regarding faults are often unbalanced. Moreover, data and models from clients may become contaminated or damaged, affecting diagnostic performance. To overcome these problems, this study proposes a stacking model for diagnosing interturn short circuit (ITSC) faults in permanent magnet synchronous motors (PMSMs). Federated learning (FL) is used to train the model to increase data security and overcome data islanding in distributed scenarios. Moreover, an improved verification strategy was adopted to select appropriate client models in each round to update the FL global model. We created a secondary server-side data set to validate the client weightings. The data set contains clean sample data for all ITSC fault categories. By calculating the fault diagnosis accuracy of the global model on the auxiliary data set, the model eliminates low-quality clients with uneven fault distributions. The improved particle swarm optimization (PSO) is used to optimize the weight coefficients of clients involved in aggregation, improving the robustness of the aggregation strategy under a joint learning system. In evaluation experiments, compared with the federated average (FedAvg) model, the proposed dynamic verification model exhibited the better diagnostic accuracy in situations of data imbalance, incurred lower communication costs, and prevented local oscillations in the model.

Multiscale Kernel-Based Residual CNN for Estimation of Inter-Turn Short Circuit Fault in PMSM.

The diagnosis of an inter-turn short circuit (ITSC) fault at its early stage is very important in permanent magnet synchronous motors as these faults can lead to disastrous results. In this paper, a multiscale kernel-based residual convolutional neural network (CNN) algorithm is proposed for the diagnosis of ITSC faults. The contributions are majorly located on two sides. Firstly, a residual learning connection is embedded into a dilated CNN to overcome the defects of the conventional convolution and the degradation problem of a deep network. Secondly, a multiscale kernel algorithm is added to a residual dilated CNN architecture to extract high-dimension features from the collected current signals under complex operating conditions and electromagnetic interference. A motor fault experiment with both constant operating conditions and dynamics was conducted by setting the fault severity of the ITSC fault to 17 levels. Comparison with five other algorithms demonstrated the effectiveness of the proposed algorithm.

A new criterion for inter-turn short-circuit fault recognition for new energy grid-connected transformer

A new criterion for inter-turn short-circuit fault recognition for new energy grid-connected transformer

Diagnosis of inter‐turn short circuit in excitation winding of pumped storage unit based on frequency‐resolved circulation spectrum

The online monitoring of inter-turn short-circuit (ITSC) faults in excitation windings of pumped storage units is easily affected by static air-gap eccentricity (SAGE) and armature response, and the early fault characteristics cannot be easily identified effectively. To address these problems, this study proposes a frequency-resolved circulation spectrum (FRCS) fault diagnosis method for ITSC faults in excitation windings. Firstly, the equation for the same phase multi-branch circulation under SAGE compound ITSC conditions is derived based on the structural characteristics of pumped storage units and electromagnetic induction relations, and obtains the characteristic harmonics of the loop current caused by ITSC faults. Secondly, this study introduces the implementation method of FRCS, considering a pumped storage unit with a generation capacity of 334 MVA as the research object. This study develops a two-dimensional finite element simulation model, calculates the magnitude of the in-phase branch circulation at different fault levels, extracts the corresponding time and frequency domain spectrum and FRCS, and verifies the effectiveness of the proposed method based on comparative experiments. Finally, based on the MJF-30-6 Synchronous Generator Experimental Platform in the Power System Dynamic Simulation Laboratory, ITSC operation at different fault levels is simulated, and the corresponding in-phase branch current data are collected for FRCS analysis. The experimental results demonstrate that the method can effectively eliminate SAGE and armature reaction interference, identify early weak ITSC faults and visualise the severity of faults in the form of pixel dots.

Inter-Turn Short-Circuit Faults Detection and Monitoring of Induction Machines Using WPT-Fuzzy Logic Approach Based on Online Condition

This paper proposes an efficient fuzzy logic-based fault detection scheme for diagnosing the inter-turn short-circuit (ITSC) faults in induction motors (IMs). The proposed approach utilizes the fast Fourier transforms (FFTs) and wavelet packet transform (WPT) for this detection of fault. To improve the efficiency and secure the operation, the proposed approach is detecting the fault in online manner. The WPT is utilized to extract the stator current signal into time-frequency domain characteristics. The variation in the amplitude of the vibration spectrum at different characteristic frequencies by FFT is utilized to identify the stator ITSC. The vibration signal is dignified by a MEMS accelerometer. The performance of the fuzzy logic fault detector (FLFD) for online condition is monitored with stator current, vibration and input speed. The performance of the proposed approach is performed at MATLAB/Simulink working site, and then the performance is compared to other existing works. The accuracy, precision, recall and specificity of the proposed approach are analyzed. Similarly, the statistical measures like root mean square error (RMSE), mean absolute percentage error (MAPE), mean bias error (MBE) and consumption time are analyzed.

Machine Learning for Inter-Turn Short-Circuit Fault Diagnosis in Permanent Magnet Synchronous Motors

This work presents an approach to detect the inter-turn short-circuit (ITSC) faults in permanent magnet synchronous motors (PMSMs) using machine learning. PMSMs possess many attractive advantages such as high efficiency and high power density. However, with their widespread applications, preventive fault diagnosis to secure safe operations and reduce maintenance costs becomes increasingly critical. To achieve this objective, in this article, machine learning is employed for fault diagnosis, wherewith minor ITSC faults can be detected in a preventive manner. Therefore, the faulty motors can be replaced early to ensure system healthy operations. Moreover, machine learning is also utilized here to classify fault levels (e.g., minor, moderate, serious faults) for reference to appropriate maintenance actions. The proposed method adopts support vector machines (SVMs) and convolutional neural networks (CNNs) for training the diagnosis model with experimental data collected from tests in the laboratory. In the SVM, appropriate features required for training are selected through analysis with the PMSM mathematical model considering ITSC faults. This allows for more efficient training with much fewer data. In contrast, CNNs are purely a data-driven approach that requires much more data for training. The accuracy of both methods is validated with another set of measured data. It is found that both methods can achieve an accuracy of 99%; however, the SVM requires much less amount of data to achieve the same accuracy. This demonstrates the advantages of the model-aided machine learning method, which would be more suitable for applications where faulty motor data are not easily acquired.

Inter-Turn Short-Circuit Faults Diagnosis in Synchronous Reluctance Machines, Using the Luenberger State Observer and Current's Second-Order Harmonic

Interturn short-circuit faults are one of the most (if not the most) harmful electrical machine failures, that if not detected and mitigated at a very incipient stage of development may involve serious consequences, both in terms of repair costs and safety. This article proposes a novel interturn fault diagnostic technique for synchronous reluctance machines based on the Luenberger state observer and current's second-order harmonic. The superiority of the proposed approach over the conventional model-based techniques is the ability to evaluate the severity of the fault as well as the efficiency in detecting faults in early stages and under various operating conditions. The effectiveness of the proposed method is demonstrated and validated through several experimental tests.

Wavelet-based multi-class support vector machine for stator fault diagnosis in induction motor

This work proposes a novel online detection scheme to diagnose incipient inter-turn short circuit fault and estimate the failure severity in induction motor. Incipient detection of the stator failure during the machine running, as well as identification of its intensity can reduce the risk of additional damage to the phase winding, improve the operational efficiency, and ensure machine availability. Hence, the incipient fault diagnosis provides a safe operating area for the motor. This work aims to specify the percentage of defective turns in the shorted winding by proposing a new mathematical parameter based on wavelet analysis, in addition to employ a multi-class support vector machine to perform the classification task. Discrete Wavelet Transform is used to analyze the stator currents after modeling the motor utilizing Clarke-Concordia transformation. From the detailed coefficients, Max and L2 norms are calculated. The adopted parameter is computed depending on the previous norms, which form the input vector to feed the classifier. The multi-class support vector machine–based one versus one algorithm is used to determine accurately the defect intensity. The acquired outcomes prove that the proposed approach, depending on the novel parameter along with multi-class support vector machine can give a robust and accurate indication about the machine status, which enables the estimation of the fault severity. To verify the competency of the methodology, various hardware experiments are carried out on the motor. The experimental results demonstrate the validity and practicability of the method, with a higher level of correctness, exceeding 96%.

Research on Interturn Short-Circuit Characteristic of the High-Speed Permanent-Magnet Machine With Gramme-Ring Windings

The conventional lap-winding high-speed permanent-magnet synchronous machine (HSPMSM) usually has a large interturn short-circuit (ITSC) current, seriously threatening the reliability. This article founds that the SC coil of the gramme-ring-winding HSPMSM is wound around the stator yoke, so the inductance of SC coil is considerable large and thus will cause a great reduction of ITSC current. The types of ITSC faults were first analyzed for a multistrand-gramme-ring-winding HSPMSM, and then, the ITSC characteristics and influencing factors were investigated based on finite-element (FE) method. The distribution of flux density is quite different from normal state, the SC current is far lower than that of the lap-winding HSPMSM, and the inductance of SC coil is fluctuant with rotation of rotor. By theoretical analysis of the inductance, the methods of increasing the height of stator yoke and using high-permeability CoFe alloy were proposed to further reduce the SC current. In addition, decreasing the strands number is beneficial to reduce the SC current density.

Novel Approach to Fault-Tolerant Control of Inter-Turn Short Circuits in Permanent Magnet Synchronous Motors for UAV Propellers

This paper deals with the development of a novel fault-tolerant control technique aiming at the diagnosis and accommodation of inter-turn short circuit faults in permanent magnet synchronous motors for lightweight UAV propulsion. The reference motor is driven by a four-leg converter, which can be reconfigured in case of a phase fault by enabling the control of the central point of the motor Y-connection. A crucial design point entails the development of fault detection and isolation (FDI) algorithms capable of minimizing the failure transients and avoiding the short circuit extension. The proposed fault-tolerant control is composed of two sections: the first one applies a novel FDI algorithm for short circuit faults based on the trajectory tracking of the motor current phasor in the Clarke plane; the second one implements the fault accommodation, by applying a reference frame transformation technique to the post-fault commands. The control effectiveness is assessed via nonlinear simulations by characterizing the FDI latency and the post-fault performances. The proposed technique demonstrates excellent potentialities: the FDI algorithm simultaneously detects and isolates the considered faults, even with very limited extensions, during both stationary and unsteady operating conditions. In addition, the proposed accommodation technique is very effective in minimizing the post-fault torque ripples.

A Novel Universal Model Considering SAGE for MFD-Based Faulty Property Analysis Under RISC in Synchronous Generators

This article proposes a new rotor interturn short circuit (RISC) fault analysis model for the electromechanical property study in synchronous generators. The specific novelty of this model lies in two aspects: First, It considers that most generators exist static air-gap eccentricity (SAGE), so it analyzes the RISC fault under normal and SAGE, making the model more versatile, and second, it takes into account not only the short circuit degree, but also the short circuit position, consequently it is more universal. By feeding the number of short circuit turns (denotes the short circuit degree), the angle between the two slots, where the short circuit takes place (denotes the short circuit position) and the detailed parameters of the generator into the model, the developing tendency of the key magnetic flux density (MFD)-based parameters can be conveniently predicted. The advantages of the proposed model primarily lie in the universality and the calculation speed. It can quickly evaluate the generator operating conditions. The phase current and the electromagnetic torque are selected in this article as the representatives of the electrical parameter and the mechanical parameter, respectively. Two-dimensional finite element analysis and experimental studies validate the proposed model.

Severity Estimation for Interturn Short-Circuit and Demagnetization Faults through Self-Attention Network.

This study presents a novel interturn short-circuit fault (ISCF) and demagnetization fault (DF) diagnosis strategy based on a self-attention-based severity estimation network (SASEN). We analyze the effects of the ISCF and DF in a permanent-magnet synchronous machine and select appropriate inputs for estimating the fault severities, i.e., a positive-sequence voltage and current and negative-sequence voltage and current. The chosen inputs are fed into the SASEN to estimate fault indicators for quantifying the fault severities of the ISCF and DF. The SASEN comprises an encoder and decoder based on a self-attention module. The self-attention mechanism enhances the high-dimensional feature extraction and regression ability of the network by concentrating on specific sequence representations, thereby supporting the estimation of the fault severities. The proposed strategy can diagnose a hybrid fault in which the ISCF and DF occur simultaneously and does not require the exact model and parameters essential for the existing method for estimating the fault severity. The effectiveness and feasibility of the proposed fault diagnosis strategy are demonstrated through experimental results based on various fault cases and load torque conditions.