Stator Winding Short Circuit Faults Research Articles

Evaluation of the Applicability of IFRA for Short Circuit Fault Detection of Stator Windings in Synchronous Machines

A machine is a device to realize the mutual conversion of electric energy and mechanical energy. This work mainly discusses the applicability of impulse frequency response analysis to detect the stator winding short circuit faults. The parameters of excitation waveform and signal sampling were carefully selected. A solid-state nanosecond pulsed power source prototype was developed for resonant excitation of windings. The proposed method was then verified on a 5 kVA, 380V, 2-pole, the salient synchronous machine through artificial fault experiments. Finally, the proposed method was compared with other traditional methods. The method has the advantages of fast measurement, rich high frequencies, high stability, and low cost. Since the principle of impulse frequency response analysis is also applicable to large synchronous machines, this method and device may also be applied to detect large synchronous machines winding short circuit faults. It can be used as an auxiliary and alternative method in the non-destructive fault detection of synchronous machines.

Detection of Stator Winding Short Circuit Faults Through Magnetic Fields In Induction Motors

In applications in the industrial world, the use of induction motors has been widely used in operation because induction motors have many advantages, although they have many advantages, induction motors themselves also have disadvantages, namely having high starting currents. In many cases the damage to the induction motor, the damage to the stator due to a short circuit, is a frequent failure, this damage can cause considerable losses because the motor can stop operation So this research will discuss about the detection of short circuit faults in the stator winding through leaky flux using a flux sensor that is placed outside the motor and placed radially and using the Fast Fourier Transform (FFT) method. Damage to the short circuit is done by reconstructing the stator winding of the induction motor. There are two variations of short circuit damage, namely short circuit winding 1 to winding 3 and short circuit winding 2 to winding 10 on an induction motor. The short circuit data is then processed using the Fast Fourier Transform method which produces data in the form of voltage to frequency. The results of the percentage of success of short circuit fault detection seen from the loaders have an average percentage of 50%, at no load conditions can detect short circuit faults by 100%. In conditions of short circuit interruption 1-3 has a success percentage of 30% and short circuit fault 2-10 by 70%. The existence of this system is expected to be able to anticipate any damage that can cause considerable and fatal losses.

Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor

1. Zhang P, Du Y, Habetler TG, Lu B. A survey on condition monitoring and protection methods for medium-voltage induction motors. IEEE Trans. Ind. Appl. 2011;47:1: 34-46. Google Scholar

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

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

Shannon Entropy Index and a Fuzzy Logic System for the Assessment of Stator Winding Short-Circuit Faults in Induction Motors

The induction motor (IM) is one of the most important elements in industry. Although IMs are robust machines, they are susceptible to faults, where the stator winding short-circuit fault is one of the most common ones. In this work, the Shannon entropy (SE) index and a fuzzy logic (FL) system are proposed to diagnose short-circuit faults, considering both different severity levels and different load conditions. In the proposed methodology, a filtering stage based on brick-wall band-pass filters is firstly carried out. After this stage, the SE index is computed to quantify the fault severity and a FL system is applied to diagnose the IM condition in an automatic way. Unlike other works that propose some types of space transformations, the proposal is only based on a filtering stage and a time domain index, requiring low computational resources. The obtained results demonstrate the effectiveness of the proposal, i.e., the SE index quantifies the fault severity, regardless of the mechanical load, and the proposed FL system achieves a positive classification rate of 98%.

Pattern recognition-based fault detection of a PM synchronous motor under stator winding short circuit faults

Stator winding short-circuit faults arising from winding insulation faults are among the most frequent faults in permanent magnet synchronous motors (PMSMs). If left undetected, such fault may be rapidly propagated, resulting in phase-to-phase or phase-to-ground faults, even the breakdown of the whole motor. A powerful fault diagnosis method requires the computation of a fault sensitive quantity and an appropriate method to get a diagnostic index and a threshold which present the edge between faulty and healthy conditions. This is particularly critical for stator short-circuit faults, especially in PMSMs which can cause catastrophic damage to the machine in a very short time. This paper proposes a new detection fault approach based on pattern recognition analysis for detecting the stator inter-turn fault in two phases. Firstly, an image of αβ stator currents in healthy and faulty conditions is composed in the 2D plane. Then, the extracted parameters according to the obtained image as areas and angles of rectangle shapes are used to detect the stator winding faults. Finally, a fault severity index (FSI) gives a slight or a serious degree of faults in PMSM. The experimental results are presented in this paper to show the usefulness of the proposed approach.

Pattern recognition-based fault detection of a PM synchronous motor under stator winding short circuit faults

Pattern recognition-based fault detection of a PM synchronous motor under stator winding short circuit faults

A Physical Faulty Model Based on Coil Sub-Element for Direct-Drive Permanent Magnet Synchronous Motor With Stator Winding Short-Circuit Faults

As winding short-circuit faults (WSF) in a direct-drive permanent magnet synchronous motor (DDPMSM) degrade its reliability and may cause serious catastrophes, fault detection and diagnosis for the DDPMSM are necessary. The performances analyses of motor under WSF are the basis of fault detection and diagnosis. An efficient and accurate faulty model is a great tool for evaluating motor performances under various WSF. Hence, this paper proposes a novel physical faulty model (PFM) based on the coil sub-element for the DDPMSM with the WSF. Fault position can be examined by differentiating the inductance and the electromotive force of the coil sub-element. This model can evaluate motor performances under various WSF, especially inter-turn short-circuit fault in different positions of the same slot, without changing the internal structure of model. The faults are set by connecting the desired taps as in the practical motor. First, the structure and parameters of the DDPMSM are reported. Second, the inductance calculation matrix, which considers the spatial position of the fault, is constructed. Then, the proposed PFM is established. The motor performances under various WSF are evaluated. Finally, the results of PFM, finite element model and experiment are compared. The results validate the correctness of the proposed PFM.

Modeling and detecting the stator winding inter turn fault of permanent magnet synchronous motors using stator current signature analysis

Many researchers have been attracted by the challenges of electrical machines fault detection, diagnosis and monitoring, which provide early warning that could help schedule necessary maintenance to avoid catastrophic consequence. Considerable papers have presented reviews and compared conditions monitoring and fault diagnosis methods for induction machines, but none for permanent magnet machines. In this paper a dynamic fault model for a rotor surface mounted PMSM machine under inter-turn insulation failure is derived. this model allows to study the location and severity of the stator winding fault using electrical circuit magnetically coupled. To achieve this objective, a precise mathematical model that can describe both healthy and fault conditions is developed. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, power spectral density (PSD) was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM.

Double-layer rotor magnetic shield performance analysis in high temperature superconducting synchronous generators under short circuit fault conditions

High temperature superconducting, HTS, synchronous machines benefit from a rotor magnetic shield in order to protect superconducting coils against asynchronous magnetic fields. This magnetic shield, however, suffers from exerted Lorentz forces generated in light of induced eddy currents during transient conditions, e.g. stator windings short-circuit fault. In addition, to the exerted electromagnetic forces, eddy current losses and the associated effects on the cryogenic system are the other consequences of shielding HTS coils. This study aims at investigating the Rotor Magnetic Shield, RMS, performance in HTS synchronous generators under stator winding short-circuit fault conditions. The induced eddy currents in different circumferential positions of the rotor magnetic shield along with associated Joule heating losses would be studied using 2-D time-stepping Finite Element Analysis, FEA. The investigation of Lorentz forces exerted on the magnetic shield during transient conditions has also been performed in this paper. The obtained results show that double line-to-ground fault is of the most importance among different types of short-circuit faults. It was revealed that when it comes to the design of the rotor magnetic shields, in addition to the eddy current distribution and the associated ohmic losses, two phase-to-ground fault should be taken into account since the produced electromagnetic forces in the time of fault conditions are more severe during double line-to-ground fault.

Detection of Stator Winding Inter-Turn Short Circuit Faults in Permanent Magnet Synchronous Motors and Automatic Classification of Fault Severity via a Pattern Recognition System

In this study, automatic detection of stator winding inter-turn short circuit fault (SWISCFs) in surface-mounted permanent magnet synchronous motors (SPMSMs) and automatic classification of fault severity via a pattern recognition system (PRS) are presented. In the case of a stator short circuit fault, performance losses become an important issue for SPMSMs. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, an artificial neural network (ANN)-based PRS was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To validate the proposed method, both simulation results and experimental results are presented.

Evaluation of stator winding faults severity in inverter-fed induction motors

Three-phase induction motor are one of the most important elements of electromechanical energy conversion in the production process. However, they are subject to inherent faults or failures under operating conditions. The purpose of this paper is to present a comparative study among intelligent tools to classify short-circuit faults in stator windings of induction motors operating with three different models of frequency inverters. This is performed by analyzing the amplitude of the stator current signal in the time domain, using a dynamic acquisition rate according to machine frequency supply. To assess the classification accuracy across the various levels of faults severity, the performance of three different learning machine techniques were compared: (i) fuzzy ARTMAP network; (ii) multilayer perceptron network; and (iii) support vector machine. Results obtained from 2.268 experimental tests are presented to validate the study, which considered a wide range of operating frequencies and load conditions.

Hybrid Model for Wound-Rotor Synchronous Generator to Detect and Diagnose Turn-to-Turn Short-Circuit Fault in Stator Windings

In this paper, a hybrid modeling approach to model synchronous generator by combining the dq0 modeling with the winding function approach has been proposed. The proposed hybrid approach improves accuracy and reduces complexity of the synchronous generator model for winding fault such as turn-to-turn short-circuit (TTSC) . This paper also proposes a simple method to model localized saturation effect due to TTSC and presents an air-gap function for synchronous machine with winding fault to improve model accuracy under high degrees of TTSC fault. Experimental validation results show that the developed synchronous generator model with TTSC fault is able to provide improved accuracy and consistent performance under wider degrees of TTSC faults in the stator windings.

Online Detection of Induction Motor's Stator Winding Short-Circuit Faults

The main objective of this paper is to develop a new technique for detecting turn-to-turn short-circuit faults in one or two stator phases of an induction motor. Hence, modeling a turn-to-turn short-circuit fault in more than one phase and a phase-to-ground fault is the first novelty of this paper for calculating phase currents under faulty conditions. This strategy uses the extracted features from the corresponding three-phase current pattern in 3-D space. Identification of faulty phases and of the severity of the fault is the outcome of this technique. This technique is also capable of detecting a phase-to-ground fault. This method just requires current sensors that are available in most drive systems to provide good controllability, and details of the machine design are not necessary. Experimental results are included to show the ability of the proposed strategy for detecting and locating phase/phases under different faults and load conditions.

Performance of a load-immune classifier for robust identification of minor faults in induction motor stator winding

Reliable detection of induction motor stator winding insulation failure at its early stages is a challenging issue in modern industry. Insulation failure between small number of turns, involving less than 5% turns of phase winding are often indiscernible and detection becomes even more complicated when motor operates at varying load levels. In line-fed motors, supply voltage unbalance is another inadvertent issue which may tend to exhibit current signature similar to stator winding inter-turn insulation failure case. The proposed work presents a robust system, to identify severity of stator winding insulation faults when an induction motor with random wound stator winding works under such operating conditions. In the present work, various features obtained from time, frequency, timefrequency, and non-linear analysis of stator currents at various stator winding short circuit faults and supply voltage unbalance conditions for different load levels have been studied. A Support Vector Machine based Recursive Feature Elimination (SVM-RFE) algorithm is used to identify the features which can provide discrimination information related to severity of fault level, independent of supply voltage unbalance and immune to load level variations. Among the extracted features, features obtained through Detrended Fluctuation Analysis (DFA) are found to be most robust for this purpose. Finally a Support Vector Machine in Regression mode (SVR) has been formed to identify winding failures employing the optimum number of features selected through SVM-RFE technique.

Diagnosis of Stator Winding Short Circuit Fault in Three Phase Squirrel Cage Induction Machine

The objective of this paper is stator winding fault detection in three phase squirrel cage induction motor using Motor Current Signature Analysis (MCSA). A prototype model of the motor has been designed to study the stator winding faults through simulation and experimentation. Simulation has been carried out using MagNet 6.11.2. The simulated stator winding faults are inter-turn, phase-to-phase, open turn in a phase. The current signatures of simulation and hardware has been compared.

FAULT PREDICTION OF DEEP BAR CAGE ROTOR INDUCTION MOTOR BASED ON FEM

The paper aims to investigate the use of the Finite Element Method for electrical and mechanical faults detection in three-phase squirrel cage, induction machines. The features of Finite Element Method are signiflcant, which consider the physical mapping of stator winding and rotor bar distribution. Therefore, modeling of the faults in stator winding, rotor bars and air-gap eccentricity will be predicated in more accurate way. In comparison with conventional methods such as coupling method, the Finite Element Method considers the complex machine geometry, material type of the bar, current and the ∞ux distributions within the electrical machines. As a result, the air-gap eccentricity and the broken bars can be modeled efiectively using this approach. Motor current signature analysis has been used to give a decision about the fault occurrence. For the broken rotor bar, frequency of the sideband components around the fundamental is used to indicate the presence of fault. However, the sideband frequencies cannot be used to recognize the stator winding short circuit and eccentricities faults, where the harmonics have approximately the same frequency over the spectrum. The amplitude of the sideband harmonic components have been used to difierentiate between them. It has been found that the inter-turn short circuit faults have a sideband harmonic component with amplitude greater than that in the case of the air-gap eccentricity faults. Also current paper introduces the detection of broken rotor bars based on stator current envelope technique.

Diagnosis of Short-Circuit Faults in Stator Winding inside Low-Voltage Induction Motor Using Impulse Voltage Test

Short-circuit faults in windings due to the deterioration of insulation is one of the most common faults in motor drive systems. An easy and effective fault diagnosis method is urgently required to ensure a highly reliable operation. This paper proposes a novel method for the diagnosis of short-circuit faults in stator winding inside a low-voltage induction motor without removing the rotor, by performing an impulse voltage test. As the rotor does not need to be removed from the motor in the novel diagnosis method, the method can be applied for practical use. In this study, first, several impulse voltage tests are carried out on the stator windings of motors. Second, the values of two features that represent the characteristics corresponding to the condition of the motor are calculated, and it is found that the shape of the feature distribution does not depend on the rotor position but on the condition of the winding. Third, the distance between the feature distribution for the healthy motor and features obtained from a target motor is calculated. On the basis of this distance, the quality of stator winding inside the induction motor is determined. The effectiveness of the proposed diagnosis method is verified by performing experiments that involve several motors with healthy and faulty windings.

Detection of stator short circuit faults in three-phase induction motors using motor current zero crossing instants

Stator faults typically have a significant share amongst the common type of faults in industrial three-phase induction (asynchronous) motors. This paper presents a motor current signature analysis (MCSA)-based diagnostics of the stator winding short circuit fault. This type of fault happens due to the destruction of the turn insulation, and can be very detrimental causing motor shutdown. Instead of traditional MCSA using the motor stator current, in this paper, analysis using the zero crossing time (ZCT) signal of the stator current is presented. The theoretical aspects of the stator short circuit detection are presented. Following that, a diagnostic algorithm utilizing the ZCT signals is proposed. Experiments are performed with real motors, healthy and with shorted stator windings. Frequency analysis of the ZCT signals from the experimental data substantiates the theoretical arguments with significant accuracy.

FE-based physical phase variable model of PM synchronous machines under stator winding short circuit faults

An FE-based physical phase variable model is proposed for PM machines under stator short-circuit faults. The parameters of the proposed model, inductances and back EMFs are determined using the FE-circuit coupled computation of the machine under the same type of fault. The parameters obtained include information on the fault location and the number of turns involved, in addition to the space harmonics and the effects of stator saturation. The FE-based phase variable model that has been developed is verified through comparison with the FE model. The significance of the work is that it introduces a feasible procedure to build a physically accurate and computationally fast phase variable model for PM machines under stator short-circuit fault conditions. Such a model is useful for the study of fault diagnosis and prognosis.