Inter-turn Short Circuit Fault Detection Research Articles

Current and flux correlation analysis for detection, location, and phase identification of stator inter-turn short-circuit fault in doubly-fed induction generator

This study proposes an innovative method for identifying and locating inter-turn short-circuit faults in doubly-fed induction generators (DFIGs), commonly used in wind-energy production. Our approach uniquely integrates data from both current and magnetic-flux monitoring, offering a complete framework for fault diagnostics. We effectively distinguish between normal and abnormal conditions in the generators by observing the patterns in current and flux waveforms. Normal conditions are marked by uniform waveforms, whereas faults create noticeable imbalances. A key feature of our research is the development of a technique to accurately determine which phase of the generator is affected by the fault, facilitating quicker and more effective repairs. To pinpoint the exact location of the fault, we use a set of four magnetic-flux sensors strategically placed around the generator. These sensors detect variations in magnetic flux, enabling the precise location of the fault. By focusing on these three essential elements—fault detection, phase identification, and exact fault localisation—our proposed algorithm significantly improves the dependability of DFIGs in wind-power applications.

A novel estimation methodology for multi-resonance equivalent inductance of transformer winding for inter-turn short-circuit fault detection

A novel, indirect measurement technique for estimating equivalent inductance associated with multi-resonance behavior of an isolated transformer winding is reported for both healthy and inter-turn short-circuit fault-condition. For healthy winding, the authors proposed measuring two unique admittance magnitude responses of the winding followed by utilizing their natural frequencies for estimating the said inductance. Here, the one response is for line-end excitation with neutral-end grounded and the other one is for two equal and opposite polarity excitations from two ends of the winding. For inter-turn shorting, driving-point admittance magnitude response with neutral-end open is also utilized. This inductance seems to be one important parameter for verifying the design constraint mainly for impulse behavior as well as for identifying inter-turn shorting of the winding. The proposed method was also verified by experimental measurements on two actual transformer windings; one of which incorporated an iron core within it. All pertinent analytical derivations and the associated results are also included in the paper.

A Search Coil Design Method of PMSM for Detection of Inter-Turn Short-Circuit Fault

Fault diagnosis systems based on search coils are widely used for detecting the inter-turn short-circuit (ITSC) fault, which is one of the most common electrical faults in electrical machines. Although various search coil structures have been proposed, there is not a systematic method proposed for the design of search coils. In addition, there is not a quantitative evaluation standard to evaluate the performances of search coils in each aspect, making it difficult to judge whether the proposed search coil structure is the optimal one. In this paper, the reliability, sensitivity and positioning ability of search coils are derived and evaluated by four defined characteristic indexes. And based on these four characteristic indexes, a systematic search coil design method based on the mutual inductance matrix is also proposed. With the proposed design method, the optimal search coil structures for different permanent magnet synchronous machines (PMSMs) are designed and optimized. It is found that the search coils designed by the proposed method have good performance in reliability, sensitivity and positioning ability. The performances of designed search coils are validated by the finite element analysis (FEA) and experiment.

Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks

Abstract Modern permanent magnet synchronous motor (PMSM) diagnostic systems are now combined with advanced artificial intelligence techniques, such as deep neural networks. However, the design of such systems is mainly focussed on a selected type of damage or motor type with a limited range of rated parameters. The application of the idea of transfer learning (TL) allows the fully automatic extraction of universal fault symptoms, which can be used for various diagnostic tasks. In the research, the possibility of using the TL idea in the implementation of PMSM stator windings fault-detection systems was considered. The method is based on the characteristic symptoms of stator defects determined for another type of motor or mathematical model in the target diagnostic application of PMSM. This paper presents a comparison of PMSM motor inter-turn short circuit fault detection systems using TL of a deep convolutional network. Due to the use of direct phase current signal analysis by the convolutional neural network (CNN), it was possible to ensure high accuracy of fault detection with simultaneously short reaction time to occurring fault. The technique used was based on the use of a weight coefficient matrix of a pre-trained structure, the adaptation of which was carried out for different sources of diagnostic information.

Stator Winding Inter-Turn Short-Circuit Fault Modeling and Detection of Squirrel-Cage Induction Motors

Stator Winding Inter-Turn Short-Circuit Fault Modeling and Detection of Squirrel-Cage Induction Motors

Induction Motor Stator Winding Inter-Tern Short Circuit Fault Detection Based on Start-Up Current Envelope Energy.

Inter-turn short circuit (ITSC) is a common fault in induction motors. However, it is challenging to detect the early stage of ITSC fault. To address this issue, this paper proposes an ITSC fault detection method for three-phase induction motors based on start-up current envelope energy. This approach uses Akima interpolation to calculate the envelope of the measured start-up current of the induction motor. A Gaussian window weighting is applied to eliminate endpoint effects caused by the initial phase angle, and the enveloping energy is obtained using the energy formula as the fault feature. Finally, by combining this with the support vector machine (SVM) classification learner, fault detection of ITSC in induction motors is achieved. The experimental results show that the average accuracy of this method reaches 96.9%, which can quickly and accurately detect ITSC faults in asynchronous motors and determine the severity of the faults. Furthermore, the average accuracy of SVM in detecting early ITSC faults under no-load conditions is 98.8%, which is higher than other classification learners, including LR, KNN, and NN. This study provides a new idea for induction motor fault detection and can contribute to induction motor maintenance.

Detection of Interturn Short-Circuit Faults in Dry-Type Voltage Transformers Using the Pulse Voltage Method

This article investigates the application of the pulse voltage method for detecting interturn short-circuit faults in the high-voltage windings of dry-type voltage transformers. We determined the characterization parameters of the high-voltage windings of dry-type voltage transformers and trends in the variation of the characterization parameters with frequency in the presence and absence of faults. The viability of using the pulse voltage method for the detection of interturn short-circuit faults in dry-type voltage transformers was analyzed. A pulse voltage test platform was designed to analyze the response voltage waveforms using pulse-oscillating voltage experiments on transformers with various short-circuit faults. The results demonstrate that during a turn-to-turn short circuit, the equivalent resistance and inductance of a high-voltage winding are significantly lowered. When employing the pulse voltage method to investigate interturn short-circuit faults, tiny inductors are connected in parallel to produce an oscillating voltage waveform that is more uniform. Upon incurring a short circuit between turns, the pulse’s oscillating voltage frequency increases. The greater the number of turns in the short-circuit fault is, the more significant the effect of the test.

PMSM Inter-Turn Short Circuit Fault Detection Using the Fuzzy-Extended Kalman Filter in Electric Vehicles

To avoid damaging the motor and its surrounding equipment, detecting Inter-Turn Short Circuit (ITSC) faults in Permanent Magnet Synchronous Motors (PMSMs) applied in electric vehicles is a crucial task. In this paper, the detection of ITSC faults in stator winding for PMSMs is carried out by means of the Extended KALMAN Filter (EKF) algorithm combined with the Fuzzy Logic Estimator (FLE). To estimate the motor parameters, including the rotor position and speed, the EKF algorithm uses the measured stator currents and voltages beside the stator resistance, which is calculated in advance using fuzzy logic and fed to the EKF. The change behaviors of the estimated parameters were then used to detect short circuit faults in the PMSM. Using Matlab/Simulink, the proposed FL-EKF algorithm was implemented and tested on a faulty PMSM controlled by Field Oriented Control (FOC). The observation of a perfect estimation of the stator resistance through the simulation helps to precisely detect the failure, and that demonstrates the sensitivity and robustness of the proposed approach.

Inter-Turn Short Circuit Fault Detection of PMSM Based on Wavelet Packet Energy Spectrum and CEEMDAN-HT

Inter-Turn Short Circuit Fault Detection of PMSM Based on Wavelet Packet Energy Spectrum and CEEMDAN-HT

Online detection of interturn short-circuit fault in induction motor based on 5th harmonic current tracking using Vold-Kalman filter

<span lang="EN-US">In this paper we propose a strategy for real-time detection of interturn short-circuit faults (ISCF) on three-phase induction motor (IM) by using a Vold-Kalman filter (VKF) algorithm. ISCF produce a thermal stress into the stator winding due to large current that flows through the short-circuited turns. Therefore, incipient fault detection is required in order to avoid catastrophic failures such as phase to phase, or phase to ground faults. The strategy is based on an analytical IM model that includes a ISCF fault in any of the phase windings and considering the </span><em><span lang="EN-US">h<sup>-th</sup></span></em><span lang="EN-US"> harmonic in the voltage supply. Based on equivalent electrical circuits with harmonics in sequence components, we propose a strategy for detection of an ISCF on IM by tracking the 5<sup>th</sup> harmonic current component using a VKF algorithm. The proposed model is experimentally validated using a three-phase IM with modified stator windings to generate ISCF. Also, the IM is feeded by a programmable voltage source to synthesize distorted voltage supply with the 5<sup>th</sup> harmonic. The results demonstrated that the positive-sequence magnitude for the 5<sup>th</sup> harmonic current component is a good indicator of the fault severity once it exceeds a threshold limit value, even under load variations and unbalanced voltages.</span>

Robust Inter-Turn Short-Circuit Fault Detection in PMSGs With Respect to the Bandwidths of Current and Voltage Controllers

Reliable inter-turn short-circuit (ITSC) fault detection is essential for permanent magnet synchronous generators (PMSGs). The harmonics in machine currents and voltages are affected by the bandwidths of dual-loop controllers, which may lead to the failure of ITSC fault detection based on the current or voltage signature analysis. In this paper, a robust method for ITSC fault detection is proposed to deal with this problem. First, the mathematical model of PMSG with ITSC fault is established. Then, the behaviors of the second order harmonics in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis currents, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis voltages and dc-link voltage with respect to the bandwidths of current and voltage controllers are investigated. After that, a reliable detection method based on two fault detection indicators and adaptive to the bandwidth changes of dual-loop controllers is proposed. The first indicator is simple and easy when only the bandwidth of current controller changes, and the other one weights and sums the second harmonics in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> -axis current, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> -axis voltage and dc-link voltage to ensure the robustness and improve the accuracy of ITSC detection when both the bandwidths of current and voltage controllers change. Finally, the effectiveness of proposed method is validated by experiments.

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.

Cost-effective process experimental studies on stator inter-turn faults detection in induction motor using harmonic RPVM and decomposition wavelet transform

Abstract This paper presents the development of a new algorithm for the diagnosis of induction motor inter-turn short circuit and mixed combined fault detection based on the reduction Park’s vector modulus and discrete wavelet transform. The proposed method is based on a combination between the RPVM approach and DWT. This new signal of the RPVM is a cost-effective alternative implementation of the Extended Park’s Vector approach through monitoring only the fluctuations of the stator current zero-crossing times in the frequency domain. Using this technique, the characteristic frequency components of the fault are extracted from only one phase stator current when compared to Park’s vector modulus and Hilbert modulus. The main advantages of the RPVM signal are to acquire the values of a single-phase stator current to allow reducing the calculation complexity and for extracting features frequencies of the faults. Thus, the reduced amount of data samples must be stored and processed. This study investigates theoretically by using the proposed RPVM signal for performing to diagnose induction motor faults through the DWT method and processed via energy eigenvalue of energies. This proposed approach RPVM-DWT is validated experimentally to demonstrate its effectiveness in that the proposed algorithm reduces the computational cost and can be used in induction motor fault detections and identifications with different fault severity conditions.

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

Detection of an Incipient Fault for Dual Three-Phase PMSMs Using a Modified Autoencoder

For the detection of incipient interturn short-circuit (IITSC) faults of machines without shutting them down, there are still shortcomings of insufficient incipient fault features and a high false alarm rate. This is especially the case for dual three-phase permanent magnet synchronous motors (PMSMs) with complex winding structures, and this kind of incipient fault detection is more complicated. To solve this detection difficulty, an IITSC detection method for dual three-phase PMSMs is proposed based on a modified deep autoencoder (MDAE). This autoencoder (AE) adopts an improved distribution metric combined with the maximum mean discrepancy (MMD) and the maximum covariance discrepancy (MCD) to extract the fault feature from the common features, which can improve the feature difference between the normal state and the incipient fault state. Then, the permutation entropy of the extracted features is calculated to detect the IITSC faults. The results illustrate that this method can not only detect IITSC faults online effectively and robustly, but also reduce the false alarm rate of the fault detection for dual three-phase PMSMs.

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 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.

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

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

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.