Stator Winding Inter-turn Short Circuit Research Articles

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

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

Stator Winding InterTurn Short-circuit Fault Detection in WRIM Using Rise and Fall Times of Stator Currents

Stator Winding InterTurn Short-circuit Fault Detection in WRIM Using Rise and Fall Times of Stator Currents

The Effect of Stator Inter-Turn Short-Circuit Fault on DFIG Performance Using FEM

Doubly-Fed Induction Generators (DFIG) are operated for wind energy production, and as their capacity is increasing, their safety and reliability become more important. Several faults affect the performance of DFIG. The stator winding Inter-Turn Short-Circuit Fault (ITSCF) is one of the most prevalent electric machine failures. This study examined the stator ITSCF effects on DFIG performance for different cases. The DFIG was designed and engineered using the Finite Element Method (FEM) and the Maxwell software, and was examined in healthy operation and four defective cases with various Numbers of Inter-Turn Short Circuit Faults (NITSCF): 4, 9, 19, and 29. These models allowed the examination of the effects and the comparison of each case separately from the healthy state. The comparison was plotted in Matlab to show the effects of the faults. The novelty of this study was that it investigated the effects of different NITSCF on the performance of DFIG and not only on their effect on the stator current and distribution of magnetic flux density. A better understanding of the short circuit effects on the performance of the DFIG can be exploited for subsequent implementations of early fault detection systems.

Electromagnetic Torque Characteristics Analysis of Nuclear Half-Speed Turbine Generator with Stator Winding Inter-Turn Short Circuit Fault

To research the variation characteristics and influence factors of air gap magnetic field and electromagnetic torque of nuclear power half-speed turbine generator with stator winding inter-turn short circuit, the two-dimensional transient electromagnetic field of generator-external circuit-grid coupling mathematical model is established by field-circuit-network coupling method. Taking the national laboratory’s generator as an example, and comparing the simulation results of the inter-turn short circuit of the armature winding with experimental data, the correctness of the model is validated. On this basis, the simulation model of a 1407MVA nuclear power half-speed turbine generator is built. Then the stator winding inter-turn short circuit fault is simulated when the generator is connected to the grid with rated load. The characteristics of air gap flux density and electromagnetic torque are obtained before and after fault, as well as, the relationship between their each component and the fault degree is obtained. At the same time, the variation law of air gap flux density and electromagnetic torque is obtained when the relative position between fault winding and magnetic pole changes. The theoretical reference for fault diagnosis and localization using harmonic electromagnetic torque after fault is provided.

Observer-based IM stator fault diagnosis: Experimental validation

In this paper, an experimental validation of an efficient approach to the Fault Detection and Isolation (FDI) of Induction Motor (IM) is proposed. The problem of Inter-turn short circuits (ITSC) in the stator windings is addressed. By introducing fault factors in the IM model an observer-based residual generator is designed, allowing the detection of ITSC in stator windings. The residual generator is built around an extended Kalman Filter (EKF) in order to estimate state variables and fault factors, which permits the evaluation of the severity of the fault. To overcome the problem of tuning the EKF a PSO algorithm is developed. It carries out a heuristic search of the noise matrices by optimizing a cost function. The proposed solution is validated by computer simulations and by real-time implementation on dSPACE 1104 Digital Signal Processor (DSP) test-bench under the healthy and the faulty conditions of IM. To perform tests under faulty conditions, an IM with customized design is built and the stator is rewound permitting to create ITSC. The results reveal the quick detection of the faults, the quantification of its severity and confirm the efficacy of this observer-based FDI algorithm.

Research on Electromagnetic Noise of Claw Pole Alternators Under Stator Winding Interturn Short Circuit Faults

This paper proposes a novel simulation method to analyze the characteristics of the electromagnetic noise of claw pole alternators under stator winding interturn short circuit faults. For the two types of faults: short circuit between two wires of the same phase and short circuit between turns of the same wire, a general circuit model built in Matlab/Simulink was introduced to obtained the phase current and output current signals. The phase current was then used as current source and the claw pole alternator was simulated as an electric motor rather than a generator to analyze the magnetic force and electromagnetic noise by using 3-D finite element method and boundary-element method, respectively. A noise bench test was conducted to verify the simulation results. It is shown that under the stator winding interturn short circuit faults, the output current harmonics and electromagnetic noise of the three-phase 12-pole/36-slot claw pole alternator would increase greatly. The 12th order noise replaces the 36th order noise as the most dominant order noise at low speed. The method proposed in this paper can also be used to diagnose the stator winding interturn short circuit faults of claw pole alternators.

Real time observer-based stator fault diagnosis for IM

This paper proposes a delta connected IM model that takes the Stator winding Inter-Turn Short Circuit (SITSC) fault into account. In order to detect the fault and evaluate its severity, an observer based FDI method is suggested. It allows the generation of residual using extended Kalman filter (EKF). To overcome the problem of the EKF initialization, the cyclic optimization method is applied to determine its tuning parameters. The advantage of the proposed approach is the real-time quantification of the fault severity and the quick fault detection. Using numerical simulation under both the healthy and the faulty conditions, the proposed IM model and EKF-based FDI approach are confirmed. Experimental results obtained by a real-time implementation on test-bench validate the simulated results.

Fault feature extraction of rotor broken bar and stator winding inter-turn short circuit of asynchronous motors

In order to accurately identify the stator and rotor faults of three-phase squirrel cage induction motor, it is necessary to extract the fault characteristics of the motor accurately and quickly. A normal induction motor, the stator phase current only have the fundamental component in theory, but it will produce harmonic components of different frequencies or change the energy of the original harmonic components when a fault occurs. Convert the fault current to frequency domain and get its spectrum, will be found that the amplitude of fundamental current is very large and the amplitude of harmonic current is very small, and their frequency is very close when the rotor bar is broken. Because of the influence of fundamental current, it is difficult to identify the fault feature components in the spectrum. In this paper use a hybrid genetic algorithm based on Nelder-Mead simplex method to estimate the fundamental current, obtain the expression of fundamental wave current and remove it from the fault current. In this way, the fault characteristics of motor stator or rotor can be obtained.

Detection of Stator Winding Inter-Turn Short Circuit Fault in Induction Motor Using Vibration Signals by MEMS Accelerometer

This paper presents a novel method for detecting stator winding inter-turn short circuit fault in a three-phase induction motor by vibration signature analysis using non-electrical contact type microelectromechanical systems (MEMS) accelerometer. The present work detects inter-turn short circuit by vibration analysis in a three-phase induction motor running under different load conditions. Theoretical analysis of stator winding under inter-turn short circuit condition has been presented. Experimental investigations have been carried out to detect inter-turn short circuit fault by vibration signature analysis using a MEMS accelerometer with three-phase induction motor operating under different conditions. The spectral analysis of motor vibration has been carried out using radix-2 decimation in time fast Fourier transform algorithm. The appearance of particular characteristic frequency components in vibration spectrum indicates stator winding inter-turn short circuit faults. The experimental results are found to closely match with the theoretical values, indicating that MEMS accelerometers can be successfully employed for the detection of stator winding inter-turn short circuit fault in induction motors.

Detection of time-varying inter-turn short-circuit in a squirrel cage induction machine by means of generalized regression neural network

PurposeDiagnostics of electrical machines is a very important task. The purpose of this paper is the presentation of coupling three numerical techniques, a finite element analysis, a signal analysis and an artificial neural network, in diagnostics of electrical machines. The study focused on detection of a time-varying inter-turn short-circuit in a stator winding of induction motor.Design/methodology/approachA finite element method is widely used for the calculation of phase current waveforms of induction machines. In the presented results, a time-varying inter-turn short-circuit of stator winding has been taken into account in the elaborated field-circuit model of machine. One of the time-varying short-circuit symptoms is a time-varying resistance of shorted circuit and consequently the waveform of phase current. A general regression neural network (GRNN) has been elaborated to find a number of shorted turns on the basis of fast Fourier transform (FFT) of phase current. The input vector of GRNN has been built on the basis of the FFT of phase current waveform. The output vector has been built upon the values of resistance of shorted circuit for respective values of shorted turns. The performance of the GRNN was compared with that of the multilayer perceptron neural network.FindingsThe GRNN can contribute to better detection of the time-varying inter-turn short-circuit in stator winding than the multilayer perceptron neural network.Originality/valueIt is argued that the proposed method based on FFT of phase current and GRNN is capable to detect a time-varying inter-turn short-circuit. The GRNN can be used in a health monitoring system as an inference module.

Stator Winding Interturn Short-Circuit Faults Severity Detection Controlled by OW-SVPWM Without CMV of a Five-Phase FTFSCW-IPM

This paper proposed a method to detect different interturn short-circuit fault severity by analyzing zero-sequence current for a new five-phase fault-tolerant fractional-slot concentrated winding interior permanent magnet motor (FTFSCW-IPM). To eliminate the effect of common-mode voltage in single dc power supply application, fault severity analysis is conducted based on an open-end winding (OW) space vector. This new method is verified by simulation and experimental results in a five-phase FTFSCW-IPM OW system.

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.

Implementation of PMSM Inter-turn Short Fault Detection Using Frequency Analysis of Stator Currents

Abstract: This paper deals with the fault detection of the Permanent Magnet Synchronous Machine (PMSM) stator winding interturn short circuit. The fault detection method is based on Motor Current Signature Analysis (MCSA). Key part of paper describes implementation of the spectral analysis fault detection method cooperated with the field oriented control (FOC) algorithm on Cortex-M4 microcontroller. This control system was utilized with low voltage PMSM with the interturn short fault emulation. Experimental results measured on PMSM test bench are important part of this paper.

Double Fault Parameters Estimation of Motor Based on Four-Order Cumulate of MUSIC

In this thesis, fault characteristic frequency can not be accurately detected if they are drowned by noise. A novel method which is Multiple Signal Classification (MUSIC) based on four-order cumulate is provided to diagnose motor broken rotor bars and stator winding inter-turn short circuit. Because four-order accumulation is able to depress noise meanwhile fault information can be obtained accurately through this way presented even with small samples. Simulation results shown that the method were higher in resolution of frequency, more accurately in fault detection and less in computational complexity.

Detection and Analysis of Stator Winding Inter-Turn Short Circuit Fault in Permanent Magnet Linear Synchronous Motor

A two-dimensional finite element model of PMLSM is build based on the finite element analysis software Magnet to research the diagnosis of stator winding inter-turn short circuit fault in PMLSM. The velocity, thrust, the stator current performance curve are obtained by simulation using Magnet when PMLSM is normal and under different extent inter-turn short circuit fault, the harmonic content of speed and thrust are analyzed using Matlab / Simulink , the conclusion that the thrust of the harmonic content is used as the Permanent Magnet Linear Synchronous Motor (PMLSM) stator inter-turn short circuit fault feature is proposed , which provided a basis for detection of stator winding inter-turn short circuit fault in PMLSM.

Wing Technique: A Novel Approach for the Detection of Stator Winding Inter-Turn Short Circuit and Open Circuit Faults in Three Phase Induction Motors

This paper presents a novel approach based on the loci of instantaneous symmetrical components called "Wing Shape" which requires the measurement of three input stator currents and voltages to diagnose interturn insulation faults in three phase induction motors operating under different loading conditions. In this methodology, the effect of unbalanced supply conditions, constructional imbalances and measurement errors are also investigated. The sizes of the wings determine the loading on the motor and the travel of the wings while their areas determine the degree of severity of the faults. This approach is also applied to detect open circuit faults or single phasing conditions in induction motors. In order to validate this method, experimental results are presented for a 5 hp squirrel cage induction motor. The proposed technique helps improve the reliability, efficiency, and safety of the motor system and industrial plant. It also allows maintenance to be performed in a more efficient manner, since the course of action can be determined based on the type and severity of the fault.

Research on Stator Winding Inter Turn Short-Circuit Faults of Aeronautical Fault-Tolerant Machine Based on Maxwell 2D

Stator winding inter turn short-circuit fault is one of the most common internal faults of fault-tolerant machine, which can disconnect the fault phases and keep operating correctly in the event of a failure. Stator winding short-circuit fault model is established through analysis. Based on finite element method, the high-power density fault-tolerant machine internal magnetic field simulation and analysis is implemented using Maxwell2D and induced voltage frequency spectrum is analyzed by FFT method. The characteristics of stator winding short-circuit faults are summarized, which lay a solid foundation for fault-tolerant machine earlier faults prediction and winding switching.

F311 THE ANALYSIS OF GENERATOR EXCITATION CURRENT HARMONICS ON STATOR AND ROTOR WINDING FAULT

When generator operates normally, armature reaction magnetic field synchronously rotates with rotor, and rotor winding won't induce additional harmonic current. When stator winding and rotor winding inter-turn short circuit fault has happened, armature reaction magnetic field non-synchronously rotates with rotor. So additional harmonic current is induced in rotor winding. Through analyzing electromagnetism characteristic on stator and rotor winding fault respectively, it is known that the additional excitation current harmonic on stator winding fault mainly is twice electrical frequency, and that on rotor winding fault is the function of air-gap excitation magnetic field harmonic frequency, poles' pairs of generator and rotor mechanical revolution. Finally, the excitation current of MJF-30-6 synchronous non-salient pole generator is acquired on stator and rotor winding inter-turn short circuit fault respectively, the results of verification show that the theory analysis is right.