Inter-turn Short Circuit Fault Research Articles

Online Interturn Short-Circuit Fault Diagnosis in Induction Motors Operating Under Unbalanced Supply Voltage and Load Variations, Using the STLSP Technique

It is well known that the most common reason for electrical machines breakdown is the stator windings’ fault occurrence. Indeed, this type of fault represents almost 40% of faults occurring in induction machines. One of the major causes of stator defects is the occurrence of interturn short-circuit (ITSC) faults that have critical and dangerous effects on the motor itself, as well as on the related electrical equipment. Therefore, early detection and a precise severity estimation of the occurrence of ITSC faults for all working conditions can prevent failure breakdowns and increase reliability and safety of industrial facilities. In this context, this article proposes an efficient online diagnostics method based on calculating and monitoring a pertinent severity factor defined as the ratio between the zero and positive voltage symmetrical components. The online implementation of this method is performed on a LabVIEW environment, using the short-time least square Prony's (STLSP) method. It does not need any estimation of motor parameters and it requires only voltage sensors. Several tests under healthy and faulty conditions are carried out on a three-phase 3-kW induction motor. The obtained results demonstrate the effectiveness of the proposed method for diagnosing the occurrence of ITSC faults with high reliability, fastness, and high precision, even under load variations or unbalanced supply voltage.

A Novel Control-Independent Online Fault Diagnosis of Interturn Short Circuits in SRMs Using Signal Injection Technique

Most of the existing diagnosis techniques for interturn short circuits (ITSCs) in switched reluctance motors (SRMs) suffer from three major issues. 1) The problem of lower sensitivity where the system cannot detect an ITSC if a lower number of turns are short-circuited; 2) Interference of load variation on the detection reliability in which the fault index might initiate false alarm without any fault; 3) The dependency of the diagnosis system on the control strategy on which the motor is operating. This article proposes a novel method to diagnose ITSCs in SRMs independent of control schemes with better sensitivity and reliability using the signal injection technique. In SRMs, not all the phases take part in the torque production at any instant. The idle or inactive phase is injected with a high-frequency (HF) voltage signal for a preselected injection ratio within an electrical cycle. Any change in the winding parameters owing to an ITSC fault is deciphered by monitoring the HF currents generated in the phase windings. The results are validated experimentally for a four-phase 8/6 SRM employing two control strategies: 1) chopped current control and 2) angle position control.

On Bayesian Optimization-Based Residual CNN for Estimation of Inter-Turn Short Circuit Fault in PMSM

Interturn short circuit (ITSC) fault diagnosis at its early stage is very essential to improve the security of permanent magnet synchronous motors. In this article, a Bayesian optimization (BO) based residual convolutional neural network (CNN) algorithm for ITSC fault diagnosis was proposed. There are mainly two aspects of the contributions. First, it is a challenge to apply a conventional CNN on time-series signal tasks as they can only look back on history with a liner size, which will limit the network depth. Moreover, to obtain enough characteristics from acquired signals, the proposed algorithm requires adequate network depth, which may result in degradation difficulties. To solve these problems, a residual connection is embedded into the dilated CNN. Second, with the increase of network depth, the possible combination of hyperparameters will grow geometrically, thus the hyperparameters tuning will cost considerable time. To overcome this, the BO algorithm is adopted to select the optimal hyperparameter combination automatically. To demonstrate the effectiveness of the proposed algorithm, a motor fault experiment with various operating conditions was conducted on the motor that can be set for 17 fault severity levels. The test results and comparisons with other five algorithms show the advantage of the proposed algorithm.

Interturn Short-Circuit Fault Diagnosis of Interior Permanent Magnet Synchronous Motor for Electric Vehicle Based on Search Coil

This article proposes an interturn short-circuit fault diagnosis method for permanent magnet synchronous machines for electric vehicles based on search coils (SC) with a unique structure. In this proposed structure, the SCs are uniformly wound in the main flux direction of each phase, and the number of SCs is twice the number of phases, resulting in significant cost savings. Based on this structure, a novel fault feature based on the negative-sequence components of the second carrier frequency sideband harmonics in the voltage signal of the SC is proposed. As a result, this fault characteristic frequency component is not only more obvious than the fundamental component used in the traditional SCs method but also can be transformed to a dc indicator by two coordinate transformations, which is conducive to online implementation in both stationary and nonstationary conditions. Finally, experiments are presented to verify the proposed diagnosis strategy.

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

Comparative study of field-oriented control of an induction machine with inter-turn short-circuit in healthy and faulty cases

The vector control of an induction machine is among the most used controls, the inter-turn short-circuit fault is a very common fault in this type of machine. Vector controllers use current regulators that are highly insensitive to internal disturbances and can therefore mask the onset of a short-circuit fault. In this article we propose simulations allowing to study the behavior of the indirect field-oriented control when an inter-turn short-circuit fault appears. The primary aim of this article is a comparative study of the effect of this type of defect on estimated quantities such as rotor flux, slip and electromagnetic torque. The results of the simulation show that these estimated quantities can be used as fault indicators instead of the stator currents. The results of this comparative study also show that the ripple of the estimated rotor flux is a very good indicator of this type of fault in this type of regulator-machine association.

Scaling Effect On Inter-Turn Short-Circuit Fault of PM Machines for Wind Power Application

This paper proposes a general analytical model of surface-mounted permanent magnet (SPM) machines with series-connected coils under inter-turn short circuit (ITSC) fault. One prominent feature of this fault model is that the air-gap and slot-leakage components of inductances under fault are calculated separately, and the influences of pole number and spatial distribution of coils have been considered in the calculations. In the model, the winding function approach (WFA) is used to calculate the air-gap inductance components by considering all space harmonics whilst slot-leakage inductance components are obtained by using slot permeance method. The proposed fault model built in Matlab/Simulink is validated by time stepping FE simulations for a 3 kW 96-slot 32-pole SPM machine. The fault model has acceptable accuracy and is suitable for the fast evaluation of fault performance of SPM machines and its accuracy considering core saturation can be improved using FE-based results. Other power ratings (0.5 MW and 3 MW) have also been investigated to study the scaling effect on machine fault-tolerant capability. A 12-slot 4-pole small scale prototype has been built to validate the developed fault models.

Characteristics Analysis of Field Winding Inter-Turn Short-Circuit Fault for Large-Capacity Synchronous Condenser Based on Stator Winding Asymmetric Branch Current

Characteristics Analysis of Field Winding Inter-Turn Short-Circuit Fault for Large-Capacity Synchronous Condenser Based on Stator Winding Asymmetric Branch Current

State Estimation MRAS and Identification of Stator Winding Phase Fault Detection of the PMSG in Wind Energy Based on the Sliding Mode Control

Abstract This paper proposes a method for the diagnosis of stator inter-turn short-circuit fault for permanent magnet synchronous generators (PMSG). Inter-turn short-circuit currents are among the most critical in PMSG. For safety considerations, a fast detection is required when a fault occurs. This approach uses the parameter estimation of the per-phase stator resistance in closed-loop control of variable speed of wind energy conversion system (WECS). In the presence of an incipient short-circuit fault, the estimation of the resistance of the stator in the d-q reference frame does not make it possible to give the exact information. To solve this problem, a novel fault diagnosis scheme is proposed using parameter estimation of the per-phase stator resistance. The per-phase stator resistance of PMSG is estimated using the MRAS algorithm technique in real time. Based on a faulty PMSG model expressed in Park’s reference frame, the number of short-circuited turns is estimated using MRAS. Fault diagnosis is on line detected by analysing the estimated stator resistance of each phase according to the fault condition. The proposed fault diagnosis scheme is implemented without any extra devices. Moreover, the information on the estimated parameters can be used to improve the control performance. The simulation results demonstrate that the proposed method can estimate the faulty phase.

Application of Fault Overlay Method and CNN in Infrared Image of Detecting Inter-Turn Short-Circuit in Dry-Type Transformer

Inter-turn short-circuit (ITSC) faults do not necessarily produce high temperatures but have special heat distribution and characteristics. Therefore, a new recognition solution for diagnosing faults is proposed, based on the fault overlay method, and the convolutional neural network (CNN) is trained to achieve the automatic identification of infrared images. In this method, through the coverage of layers, the proposed image augmentation method is carried out and simulates the fault data of increasing training. We produce 43 fault traces through the fault overlay method on the three-phase winding of a transformer and use 90 infrared images of transformers in normal operation combined with them to enhance the amount of image data. The fault recognition ability is realized based on CNN model training, including analysis of experimental results of grayscale and color images, and Gaussian noise. In the test of the practical case, a short-circuit test of the 11.4 kV dry-type transformer is carried out, and the ITSC fault is identified when the load is about 15%. The fault characteristic block on this thermal image is 36.3 degrees, which verifies the identification available by this method, and has a certain reference value for the development of infrared image diagnosis technology for power equipment.

Fault indicator based on stator current under early stage SISC in synchronous condensers

In this study, the stator current characteristics of synchronous condensers for stator interturn short-circuit (SISC) fault in the early stage are investigated using theoretical derivation, finite-element analysis (FEA) and experimental research. In contrast to previous studies, this study considers the real operation characteristics of synchronous condensers that need to change the exciting current to meet the reactive power demand of the power grid. Thus, a new SISC fault indicator (FI) based on the stator current is proposed in this study. The new FI can identify incipient SISC and suppress the interference of the exciting current variation. A customized synchronous machine and TTS-300-2 synchronous condenser are simulated using FEA to demonstrate the effectiveness of the FI. Furthermore, experimental tests are conducted in a customized synchronous machine to prove the practical significance of the studies.

CAD system for inter-turn fault diagnosis of offshore wind turbines via multi-CNNs & feature selection

Condition monitoring, fault diagnosis, and scheduled maintenance of wind turbines (WTs) are becoming a necessity to maximize their economic benefits and reduce their downtime. One of the critical faults in WTs is inter-turn short-circuit faults (ITSCF) in their rotating machines. Being dependent on higher wind speeds, offshore wind farms are more efficient than onshore ones, yet at the cost of higher ITSCF, more maintenance requirements, and difficulty in accessibility. Thus, they require an efficient non-contact fault diagnosis technique to enable operators to inspect all WT components with minimal contact. Among existing fault detection technologies, Infrared thermography (IRT) is considered as a non-invasive and non-destructive anomaly detection technique based on capturing thermal images by IR cameras, making it suitable for offshore harsh environments. Meanwhile, deep learning (DL) fault diagnosis approaches have proven to be promising intelligent tools with minimum human labor. This paper proposes an efficient and robust ensemble DL-based diagnosis method for ITSCF detection in induction rotating machines, besides identifying fault locations and short-circuit severity. The proposed technique is quite convenient for early diagnosis of offshore WT generator faults since it depends on IRT technology. Compared to previous studies, the proposed approach outperforms its counterparts since it integrates eight DL models, rather than using a single model, thus merging all their structural benefits altogether. Moreover, a cascaded feature fusion and selection procedure is introduced. First, the deep features extracted from the DL models are fused, then the most influential features are selected using a hybrid feature selection scheme. Thus, the classification accuracy is enhanced to reach 100% and meanwhile, the input features’ size to the classifier is reduced, decreasing classification complexity and training time. Finally, no clustering or segmentation phases are required in the proposed technique, resulting in a further decrease in the diagnosis time and computation burden.

Impact of stator interturn short circuit fault on shaft voltage in a synchronous generator

AbstractShaft voltage often exists in synchronous generators due to the assembly error or various faults after the long‐term performance. The article investigates the shaft voltage characteristic under the stator interturn short circuit (SISC) condition. Different from other studies, this article mainly considers the mapping relationship between the amplitude–frequency characteristics of the shaft voltage and the SISC degrees. The detailed shaft voltage expressions are first derived based on the magnetic flux density under the normal condition and SISC condition. Then the finite element analysis and the experiment is studied on a CS‐5 prototype synchronous generator with two poles in order to validate the theoretical formula. The result shows that there is no shaft voltage in the normal condition, while the shaft voltage is generated obviously under the SISC condition. Meanwhile, the frequency component of the shaft voltage is mainly composed of odd harmonics including the first, third and fifth harmonics.

Mitigation of Interturn Short Circuit Fault Based on Axial Split Phase Permanent Magnet Synchronous Machine

In order to solve the problem that three-phase PMSM cannot operate under interturn short circuit (ITSC) fault, an ITSC fault mitigation scheme based on three-phase axial split phase permanent magnet synchronous machine (ASP-PMSM) is proposed in this paper. The ASP-PMSM has inherently superior fault isolation capability among phases, high inductance and low copper loss induced by ITSC current, which greatly reduces the fault severity and difficulty of fault mitigation from machine structure. The inherently low fault severity of ASP-PMSM leaves space for postfault torque production. Therefore, it does not need the phase current to be totally injected to d-axis. As a result, the operation of three-phase PMSM under ideal ITSC fault can be realized while maintaining more than half of rated torque. In order to find out the most serious fault condition for different winding types, the influence of the number of parallel strands and the SC turn ratio on the severity of ITSC fault is investigated by theoretical analysis (TA) and finite element analysis (FEA) for the first time. And the effectiveness of the fault mitigation for the ASP-PMSM with different number of parallel strands is investigated by FEA. Finally, a three-phase prototype of ASP-PMSM is served as an example to verify the validity of analysis above. The ITSC current and temperature at different SC turn ratio and operating conditions are tested. The test results are consistent with that of TA and FEA. It is verified that the proposed fault mitigation scheme can realize the postfault operation of three-phase PMSM.

Online Short-Circuit Current Estimation of Permanent Magnet Motors

In this article, online short-circuit estimation of permanent magnet synchronous motors (PMSMs) are presented, which is essential for postfault ac drive operations. For this purpose, initially, the PMSM model with interturn short-circuit fault (ITSC) is extended to cover motors with saliency. By utilizing ITSC model voltage equations and voltage reference equations, the amplitude and phase relationship between short-circuit current and the phase reference voltage is established. In the next step, second harmonics in the reference voltage and feedback current are used to extract the short-circuit current information. In order to efficiently use ac drive microcontroller bandwidth, the proposed short-circuit current estimation method is executed after the ITSC fault is detected, not continuously. Instead of running the proposed short-circuit current estimation continuously, it is reasonable to run it after an ITSC fault detection to efficiently use ac drive microcontroller bandwidth. Therefore, two intermediate steps, a simple ITSC fault detection and severity estimation algorithms are proposed as well. The proposed technique can estimate the fault current and severity without the knowledge of fault location and valid for all PM motors. The findings are experimentally tested using two PMSMs to demonstrate the effectiveness and practicability of the proposed estimation technique.

A Simplified LPTN Model for a Fault-Tolerant Permanent Magnet Motor under Inter-Turn Short-Circuit Faults

This paper proposes a simplified lumped parameter thermal network (LPTN) model for thermal analysis under the inter-turn short-circuit (SC) faults of a five-phase fault-tolerant permanent magnet (PM) motor for electric vehicles. The proposed model can consider circumferential heat transfer, variable copper loss, and uneven loss distribution. Firstly, an analytical calculation formula of inter-turn SC current is proposed to separate the copper loss of SC turns from electromagnetic simulation, and the accuracy of the formula is verified by finite element analysis (FEA). Secondly, a simplified LPTN model is proposed, the calculation formulas of the thermal resistance are given, and the simplified principle is explained. By comparing the results of different simplifications, it is found that the error between the simplified model and the original model is small. Finally, the proposed model is verified by experiments. The results show that the simplified model can achieve not only a high calculation speed but also a high accuracy. Moreover, the proposed model is applicable to all cases of asymmetrical temperature distribution.

Vibration Analysis of the Electric Drive System with Inter-turn Short-Circuit and Gear Spalling Faults

Vibration Analysis of the Electric Drive System with Inter-turn Short-Circuit and Gear Spalling Faults

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.

Stator current identification in generator among single and composite faults composed by static air‐gap eccentricity and rotor inter‐turn short circuit

In this study, a fault diagnosis model based on magnetic density is proposed to analyse the current characteristics of synchronous generator rotor inter-turn short circuit and air gap eccentric single fault and composite fault. The model considers the influence of rotor inter-turn short circuit and air gap eccentricity on air gap magnetic density. According to two main factors, the short circuit turns and eccentric distance, are used to reflect the short circuit and eccentricity of the generator. The detailed parameter input model of the generator can easily and quickly predict the development trend of these current parameters based on the air gap magnetic density. And the phase current is selected as the current parameter. The validity of the proposed model is verified by two-dimensional finite element analysis and experimental research.