Inter-turn Fault Detection Research Articles

Inter-turn short-circuit fault detection with high-frequency signal injection for inverter-fed PMSM systems

Inter-turn short-circuit fault detection with high-frequency signal injection for inverter-fed PMSM systems

Improvement in Inter-Turn Faults Detection in Poles of Synchronous Machines by FRA Grounding Connection

Inter-turn faults are among the most common failures in electrical machines. While Frequency Response Analysis, (FRA) has been extensively used for power transformers diagnosis for the last 40 years. In this conventional, accurate and non-destructive technique for power transformer diagnosis, it is a usual practice to connect the chassis of the FRA test device to the transformer housing, normally grounded. The same practice has been applied for pole diagnosis, although no procedure has yet been established as standard since pole diagnosis using FRA is still an experimental technique. Nevertheless, this paper shows a different approach to diagnose inter-turns faults in poles by isolating the FRA chassis from the iron core. In this way, the capacity to ground has less influence, increasing FRA test sensitivity for inter-turn fault diagnosis. The method has been verified by experimental results in several spare poles belonging to 106 MVA and 40 MVA synchronous hydro-generators and also by two simulation models with both types of connections, grounded and FRA-isolated, achieving positive results.

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

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

Inter-turn fault detection of induction motors using a method based on spectrogram of motor currents

Steady-state signatures seem more reliable than incipient fault signatures for detection of inter-turn short circuit (ITSC) fault in induction motors (IMs). An efficient detection approach is proposed based on the deviation of the histogram relevant to the time–frequency plane’s image of the steady-state modal current signal from the standard normal distribution. First, the misleading frequency components like 3rd, 5th, and 7th harmonics are excluded using Kalman Filter (KF). Then, from the time–frequency spectrogram of the signal, the converted gray-level image and its histogram are obtained. A considerable deviation of the histogram distribution from a normal distribution is observed in the case of ITSC fault, used for the detection. A cumulative index including normalized skewness and kurtosis of the histogram is used as the criterion of the detection. Finally, a novel threshold setting method based on Otsu’s threshold principle is proposed, which efficiently distinct faulty conditions from healthy operations. The required data for assessment of the approach is gathered from different experiments carried out on a test bench, subjected to various fault percentages and different load levels. The results confirm the effectiveness of the proposed methodology.

An Inter-Turn Short-Circuits Fault Detection Strategy Considering Inverter Nonlinearity and Current Measurement Errors for Sensorless Control of SPMSM

Aiming at the misdiagnosis problem of the interturn short-circuit (ITSC) fault detection method caused by the inverter nonlinearity and the current measurement errors, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -axis residual voltage-based fault diagnostic strategy is proposed for sensorless control of the surface permanent magnet synchronous motor (SPMSM). The model of the three-phase wye-connected SPMSM with ITSC fault is established, and the expression of the d-axis residual voltage caused by the ITSC fault, the inverter nonlinearity, the current measurement errors and the motor parameter errors is derived. The proposed fault diagnostic strategy can eliminate the current relative gain coefficient error and locate the ITSC fault by using the eighth and second harmonics of the estimated d-axis residual voltage, respectively. The experimental results verify the effectiveness of the proposed fault diagnostic strategy.

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

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

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

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

A Voltage-Distortion-Based Method for Robust Detection and Location of Interturn Fault in Permanent Magnet Synchronous Machine

In this article, a voltage-distortion-based method is presented for robust detection and location of interturn fault (ITF) in permanent magnet synchronous machine (PMSM). The mathematical models of PMSM under healthy and fault conditions are presented. According to the mathematical model, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis voltage distortions owing to ITF are estimated through Luenberger observer. On the basis of the voltage distortions, a fault characteristic signal (FCS) is constructed for ITF diagnosis. Through analyzing FCS, the second harmonic component in FCS is chosen to construct the fault indicators for fault diagnosis. The simulation and experimental results show that the ITF diagnosis is effectively realized through the presented method. Additionally, the presented method requires no additional hardware and has good robustness against parameter mismatch and rotating speed variation.

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.

Haar-Wavelet-Based Statistical Scanning of Turn Fault in Vehicular Starter Motor

Stator interturn fault is one of the most common faults that affect motor operation. Its failure will not only prevent the vehicle from starting but may also cause other adverse problems. In this letter, an automotive system has first been modeled in MATLAB/Simulink environment. The starter motor used in this system has been simulated to undergo a stepwise interturn fault. For every shorted starter armature coil, the current data were recorded and then wavelet analysis was performed on the gathered data. The wavelet-based parameters variation was determined for every degree of short on the starter stator armature coil. Based on learning, an algorithm has been proposed and then validated with real data to detect the variation in the wavelet parameters and to help determine normal operating conditions, prefault conditions, as well as conditions during fault of the automotive system to help in early detection of starter motor interturn fault. This has been done using a square-shaped Haar wavelet function based on the Hadamard-Haar transform. Such early detection can be very helpful for automotive systems, and if such faults are left unattended, it may result in complete machine failure due to excess heat generation due to the flow of high current in the shorted coils.

Incipient Interturn Fault Detection and Severity Evaluation in Electric Drive System Using Hybrid HCNN-SVM Based Model

The importance of induction motor (IM) drives in industrial applications, especially in the renewable energy sector is unarguable due to their fast-dynamic response. Their monitoring is key to avoid downtime and economic losses. Even though a lot of research has been focused on incipient fault diagnosis in induction machines, early diagnosis in power electronic drive-fed machines is still a challenge. In this article, a novel two-level hybrid hierarchical convolution neural network with support vector machine (HCNN-SVM) is proposed for incipient interturn fault diagnosis of drive-fed machines. The first level of the proposed structure is intended to recognize the pattern of interturn fault in drive-fed IM, while the second level is developed to identify the fault severity. First, the effective features are extracted automatically using the shared layers of HCNN for fault diagnosis and fault severity evaluation at the same time. The features obtained using the HCNN are suitably used to train SVM for classification. Comparison of the results with the existing architectures, namely, HCNN and SVM, show the effectiveness of proposed hybrid method. The experimental results show that the hybrid HCNN-SVM is fast and highly accurate in identifying the interturn fault pattern and evaluating its severity.

A comparative study for stator winding inter-turn short-circuit fault detection based on harmonic analysis of induction machine signatures

This article deals with inter-turn short-circuit fault detection in stator windings of squirrel cage induction machines. The main aim is to perform harmonic analysis of different electrical signatures namely the stator phase current, external magnetic flux and electromagnetic torque at different levels of mechanical load in order to develop an efficient fault detection approach of this kind of defect in induction machines. The proposed approach is based on the analysis of saturation related harmonics at rank 3k1fs, where k1 is an odd number, and magnetomotive force (MMF)-related harmonics at rank (6k2±1)fs, with k2=1,2,… in stator phase current and stray flux and harmonics at rank 2k2fs in electromagnetic torque. The amplitudes of these last harmonics in healthy condition are compared with 3% power supply unbalance, 16.6% (40 turns) and 33% (80 turns) levels of inter-turn short-circuit fault in frequency range from 0 Hz to 2500 Hz under different levels of mechanical load. Besides, the stand-still test is also investigated in this work. Simulation study is carried out based on 2.2kW squirrel cage IM using finite element method (FEM). This method provides accurate and inexpensive tool for evaluating the performance of induction machine under healthy and faulty conditions. The obtained results demonstrate that the stray flux is the most sensitive signature to the stator winding inter-turn short-circuit fault, and it is robust against the power supply unbalance in comparison with both stator current and electromagnetic torque.

Rotor winding inter-turn short-circuit fault detection in wound rotor induction motors using Wing Technique

Rotor winding inter-turn short-circuit fault detection in wound rotor induction motors using Wing Technique

Experimental investigation in induction motors using signal processing techniques for early detection of inter-turn short circuit faults

ABSTRACT This paper presents a method based on advanced signal processing techniques for the detection of inte-rturn short circuit faults in an induction machine. The techniques presented can be classified into power spectral density estimation techniques and time-frequency techniques. The theoretical principles of these methods are presented and their performances are compared from the stator current analysis (MCSA). This article proposes a comparative study of these signal processing techniques. One is based on the power spectral density estimation techniques, such as Periodogram and Welch periodogram. The second method is based on the Hilbert transform (HT) to extract the envelope for the stator current. Then, this signal is processed via discrete wavelet transform (DWT) for determining the faulty components in the spectrum of the stator current envelope and identifying the eigenvalues of energy levels (HDWT). This technique is powerful for the non stationary signal processing. Moreover, the combination HT-DWT has assured its ability to evaluate the severity in detecting faults of the shorted turns and it is effective for nonstationary and stationary signals to extract useful information for the detection of ITSC fault. The techniques have usd experimentally data issued from the induction motor.

Intern-turn fault modeling and diagnosis in permanent magnet vernier machine using modified magnetic equivalent circuit method

PurposeThe aim of this paper is to propose the model for analyzing the electromagnetic performances of permanent magnet vernier machines (PMVMs) under healthy and faulty conditions.Design/methodology/approachThe model uses interconnected reluctance network formed based on the geometrical approximations to predict magnetic performances of the machine. The network consists of several types of reluctances for modeling different parts of machine. Applying Kirchhoffs laws in the network and the machine windings, magnetic and electrical equations are obtained, respectively. To construct the model system of equations, the electrical equation is converted into algebraic form by using the trapezoidal technique. Moreover, the system of equations must be solved by Newton–Raphson method in each step-time because of considering the core saturation effect.FindingsThe proposed model is developed based on the modified magnetic equivalent circuit (MEC) method, in which the number of flux paths in different parts of the machine can be arbitrary selected. The saturation effect, skewed slots, the desired machine geometrical parameters and various winding arrangements are included in the proposed model; therefore, it can evaluate the time and space harmonics in modeling the PMVMs. Furthermore, a pattern for inter-turn fault detection is extracted from the stator current spectrum. Finally, 2 D-finite element method (FEM) and 3 D-FEM analysis are carried out to evaluate and verify the results of the proposed MEC model.Originality/valueGenerally, the element numbers have important role in modeling the machine and calculating its performance. Hence, the proposed MEC model’s capability to choose desired number of flux paths is advantage of this paper. Moreover, the developed MEC can be used for analyzing several electrical machines, including other types of vernier machines, with simple modification.

A robust stator inter-turn fault detection in induction motor utilizing Kalman filter-based algorithm

Today, due to the widespread utilization of Induction Motors (IMs) in different industries, their condition monitoring is of great importance. Concentrating on the failures of IMs, it has been acknowledged that the stator inter-turn faults (SITFs) are the most frequent electrical failures. This paper puts forward an algorithm for SITF detection founded on Kalman Filter (KF). More specifically, the proposed algorithm employs KF to extract motor current signatures (MCS) and motor voltage signatures (MVS). Afterward, a statistical SITF index is used, based on the standard deviation of the extracted signatures. The proposed SITF index is technically robust against non-fault conditions including voltage quality problems and heavy load changes as well as has a significant performance in the presence of high measured noise-impregnated signals due to utilization of KF algorithm-based. Moreover, during source harmonic pollutions, the proposed algorithm has a very robust performance. Also, due to straightforward and low-computational mathematical basis, the proposed method is computationally efficient. The performance of the proposed method is validated with numerous simulation and experimental scenarios. The results indicate the proposed SITF index has robust performance, promising accuracy and good speed of convergence.

Performance Evaluation of Signal Processing Tools Used for Fault Detection of Hydrogenerators Operating in Noisy Environments

Signal processing plays a crucial role in addressing failures in electrical machines. Experimental data are never perfect due to the intrusion of undesirable fluctuations unrelated to the investigated phenomenon, namely so-called noise. Noise has disturbing effects on the measurement data and, in the same way, could diminish or mask the fault patterns in feature extraction using different signal processors. This article introduces various types of noise occurring in an industrial environment. Several measurements are performed in the laboratory and power plants to identify the dominant type of noise. Fault detection in a custom-made 100-kVA synchronous generator under an interturn short-circuit fault is also studied using measurements of the air-gap magnetic field. Signal processing tools such as fast Fourier transform, short-time Fourier transform (STFT), discrete wavelet transform, continuous wavelet transform (CWT), and time-series data mining are used to diagnose the faults, with a central focus on additive noise impacts on processed data. Two novel patterns are introduced based on STFT and CWT for interturn short-circuit fault detection of synchronous generators that do not need a priori knowledge of a healthy machine. Useful methods are presented for hardware noise rejection.

Negative Sequence Current Compensation for Induction Motor Stator Inter-Turn Short Circuit and Off-Diagonal Term in Sequence Impedance Matrix as Fault Indicator

This work proposes an artificial neural network supported method to establish an automatic detection of stator turn fault in induction motor. The sequence current analysis is done for turn fault condition. Various factors influencing the total measured negative sequence current such as unbalanced voltage and inherent asymmetry have been reviewed. To compensate the voltage unbalance and non-idealities in the machine, utilization of measured negative sequence current, impedance, admittance, or semi empirical formula is developed. The output of a well-trained feed forward back propagation neural network classifies the severity of fault level in stator winding. The method of considering the effects of turn faults on inter-turn fault detection improves sensitivity meanwhile reduces the prospect of misdetection.

Transformer inter-turn winding fault detection based on no-load active power loss and reactive power

An inter-turn fault in a transformer can cause severe damage to transformer windings if not detected quickly. In this paper, the fault diagnosis is discussed by examining the no-load active power loss (NLAPL) and no-load reactive power (NLRP) of the transformer. Two indices, Total harmonic distortion of active power (THDAP) and Total harmonic distortion of reactive power (THDRP), based on odd harmonic components of NLAPL and NLRP, are proposed. To validate the obtained results, the acquired outcomes of tests on a physical device are compared with the results of the finite element analysis. To do this, three-dimensional model of a single-phase transformer is simulated in the transient mode using ANSYS MAXWELL 17.02. Results show that in the presence of an inter-turn short circuit fault on the transformer high voltage (HV) windings, the low voltage side current and NLAPL increase while the variation in NLRP is very small. In addition, from the transient analysis point of view, it becomes clear that THDAP and THDRP are reduced in the presence of an inter-turn fault.