Inter-turn Short Circuit Fault Research Articles

Inter-Turn Fault Detection in PM Synchronous Machines by Physics-Based Back Electromotive Force Estimation

In this paper, the inter-turn short circuit fault detection in permanent magnet synchronous machines (PMSM) using an open-loop physics-based back electromotive force (EMF) estimator is presented. The back EMF estimator is designed based upon a current mode tracking scheme. The thermal and saturation aspects of the machine are considered in the design of the estimator. The design procedure and stability criteria of the estimator are presented in detail. The fault detection is carried out based on the difference between the estimated back EMF and a reference back EMF. A 0.8 (kW) PMSM is studied experimentally as well as numerically under different inter-turn fault and operational contingencies. The numerical modeling is accomplished by a finite-element-based model coupled with the thermal network and polluted with inter-turn fault. The acceptable agreement between the simulated and experimental result validates the modeling process. The back EMF estimator fault detection system is led to discriminative inter-turn fault signatures in a fraction of second for wide speed range even in the presence of harmonic loads and dynamic eccentricities.

PREDICTIVE MAINTENANCE BASED ON EARLIER FAULT DETECTION OF MULTI PHASE INDUCTION MACHINES USING NEURAL NETWORK ARTIFICIAL INTELLIGENT TECHNIQUES

The area of multiphase variable-speed motor drives in general and multiphase induction Motor drives in particular have experienced a substantial growth since the beginning of this century. Research has been conducted worldwide and numerous interesting developments have been reported in the literature. An attempt is made to provide a detailed overview of the current state-of-the-art in this area. The elaborated aspects include advantages of multiphase induction machines, modeling of multiphase induction machines. This paper also provides a detailed survey of the control strategies for five-phase and asymmetrical six-phase induction motor drives for the saturated model of the induction motor. However all the old researches in this field are obtained using the approximate linear model of the induction machine which is not exactly accurate because that we are not guarantee that the motor operation is not in the saturation region . These results are also included for clarifying the behavior of the five and six phase using the saturated model of induction machine as an examples of the multi phase machine. Also this paper presents an approach to induction motor fault diagnosis and condition prognosis based on neural network and adaptive neuro-fuzzy inference systems (ANFIS). The ANFIS is a neural network structured upon fuzzy logic principles, which enables the neural fuzzy system to provide the motor condition and fault detection process. This knowledge is provided by the fuzzy parameters of member ship functions and fuzzy rules. By using the neural network and (ANFIS) techniques, we can detect and locate the inter-turn short circuit fault in the stator winding of an induction motor. Simulation results are presented to demonstrate the effectiveness of the proposed method.

Experimental investigation and comparative study of interturn short-circuits and unbalanced voltage supply in induction machines

A transient model for an induction machine with stator winding turn faults on a single phase is derived using reference frame transformation theory. The negative sequence component and the 3rd harmonic are often considered as accurate indicators. However, small unbalance in the supply voltage and/or in the machine structure that exists in any real system engenders the same harmonics components. In this case, it is too difficult to distinguish between the current harmonics due to the supply voltage and those originated by inter-turn short-circuit faults. For that, to have the correct diagnosis and to increase the sensitivity and the reliability of the diagnostic system, it is crucial to provide the relationship between the inter-turn short-circuits in the stator winding and the supply voltage imbalance through an accurate mathematical model and via a series of experimental essays.

Analysis of internal winding short circuit faults in power transformers using transient finite element method coupling with external circuit equations

SUMMARYInterturn short circuit faults are the leading cause of power transformer failures. If not quickly detected, these faults usually develop into more serious faults, which would result in irreversible damage to the transformer, unexpected outages, and consequential costs. The aim of this research is to obtain a better understanding of the physical behavior of the power transformers in the presence of interturn faults as well as to discover the best indicators for detection of interturn short circuit faults. To this end, a time stepping finite element model of power transformer has been developed to analyze the transient behavior of a real power transformer when the transformer is working under winding short circuit fault conditions. The transient model, coupled with external circuit equations, allows us to simulate the dynamic behavior of the transformer with the real power supply and external loads connections. The FEM computations show the ability of the electromagnetic flux inside the transformer, transformer terminal currents, and circulating current in the shorted turns as useful monitoring parameters for transformer fault detection. Copyright © 2013 John Wiley & Sons, Ltd.

A More Sensitive Indicator of Inter-Turn Short-Circuits Fault in Stator Windings Including Unbalanced Supply Voltage

The main goal of this paper is to identify current signatures in induction machine when the inter-turn short circuit fault under unbalanced supply voltage is considered. Research indicates that the inter-turn short circuit fault will give an evident change in the first negative harmonic and third harmonic components in stator current. On the other hand, the variation in these components can also be observed when an unbalance in voltage supply occurs. The latter makes them as unreliable signatures for stator faults, particularly at an incipient stage. For that, it is so important to identify new signatures for inter-turns short-circuit which are undisturbed by the presence of voltage supply unbalance. A series of experimental tests and simulation results show the effectiveness of the present study

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.

Online detection of inter-turn short circuit faults in dry-type air-core reactor

Inter-turn short circuit faults are among the common faults occurring in dry-type air-core reactor. This paper presents an online detection method of inter-turn short circuit faults for dry-type air-core reactors by using search coils. Since this type of fault creates shorted turns in which there are high induced currents that can produce intense reversed magnetic field, the magnetic field distribution is no longer symmetrical. The variety of magnetic field from the normal condition to the fault condition can be monitored by search coils. Based on coupled field circuit method, the magnetic field distribution of the reactor and the induction voltage of the search coils are computed in different conditions. The proper mounted positions of search coils are suggested. The proposed method has been verified to be effective and easy for implementation by experimentation.

Feature extraction of induction motor stator fault based on particle swarm optimization and wavelet packet

To effectively extract the interturn short circuit fault features of induction motor from stator current signal, a novel feature extraction method based on the bare-bones particle swarm optimization (BBPSO) algorithm and wavelet packet was proposed. First, according to the maximum inner product between the current signal and the cosine basis functions, this method could precisely estimate the waveform parameters of the fundamental component using the powerful global search capability of the BBPSO, which can eliminate the fundamental component and not affect other harmonic components. Then, the harmonic components of residual current signal were decomposed to a series of frequency bands by wavelet packet to extract the interturn circuit fault features of the induction motor. Finally, the results of simulation and laboratory tests demonstrated the effectiveness of the proposed method.

A Modified Probabilistic Neural Network-based Algorithm for Detecting Turn Faults in Induction Machines

In this paper, an intelligent technique for detecting the turn fault and supply voltage unbalances in an induction machine is proposed under varying load conditions. The proposed probabilistic neural network (PNN) technique is based on well-established statistical principles rather than heuristic approaches that are adopted in the multi-layer perceptrons. The PNN is derived from the Bayes decision strategy and nonparametric kernel-based estimators of probability density functions. The machine model is developed for inter-turn short circuit fault and it is compared with its a priori models for its robustness in detecting the fault at incipient stages under various operating conditions of the machine. This algorithm is proposed for first time and training, testing, and validation results using the method appear promising and easily realizable in the industries.

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.

Simulation of emerging faults in electric machines

The article presents simulation based on finite element technique of the most common faults in three-phase squirrelcage induction motors. Faults examined in this paper are: rotor broken bars and inter- inter turns short-circuit in stator windings. Fault in rotor broken bars was simulated taking two consecutive bars to see how the flux density is affected. Interturn short-circuit faults was simulated taking 40% inter-turn short-circuit of the windingin one phase. Simulations were performed using finite element software FEMM to obtain the flux density waveform in the airgap and in the electromagnetic core. Results from simulations were exported to MATLAB for further process and analysis. Flux density waveforms were plotted and processed by FFT analysis to observe harmonic distribution.

Impact of Rotor Inter-Turn Short-Circuit on Generator Rotor Force

This paper analyzes the calculation method of unbalanced electromagnetic force by rotor winding inter-turn short-circuit fault, analytical calculating method neglects saturation, cogging and other factors, so the calculation accuracy is not high, in order to accurately calculate the magnetic field and the unbalanced electromagnetic force, this paper propose to analyze generator magnetic field by a finite element software Ansys, the obtained magnetic field data are further used to analyze the distributed electromagnetic force and the total force that generator rotor suffered, electromagnetic force distribution law of different slots, different levels of inter-turn short-circuit are got, which provide a basis for further studying the full force on generator rotor.

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.

Modeling of Surface-Mounted Permanent Magnet Synchronous Motors With Stator Winding Interturn Faults

This paper develops and analyzes a parametric model for simulating healthy and faulty surface-mounted permanent magnet synchronous motors. It allows studying the effects of stator winding interturn short-circuit faults. A relevant feature of the developed model is that it deals with spatial harmonics due to a nonsinusoidal rotor permanent magnet configuration. Additionally, the proposed model is valid for studying the behavior of these machines running under nonstationary conditions, including load or speed variations. Stator current spectra obtained from simulations performed by applying the proposed model show a close similitude with experimental results, highlighting the potential of such a model to understand the effects of stator winding failures on the current spectrum and allowing it to carry out an automatic diagnosis of such faults.

Numerical modelling of transformers interturn faults and characterising the faulty transformer behaviour under various faults and operating conditions

Interturn short circuit faults are the leading cause of power transformer failures. If not quickly detected, these faults usually develop into more serious faults that would result in irreversible damage to the transformer, unexpected outages and the consequential costs. This contribution is aimed at obtaining a better understanding of physical behaviour of power transformers in the presence of interturn faults as well as extracting several features that would be useful to specify the faults. To this end, a circuit-coupled time-stepping finite-element model (FEM) of power transformer has been developed to characterise the transient behaviour of a real power transformer when the transformer is working under winding short circuit fault conditions. An experimental set-up consisting of the same transformer used with the FEM transformer simulation was used to validate the FEM of the faulty transformer. The results of the experiments demonstrate the remarkable ability of the model to reproduce the real behaviour of the transformer with interturn winding faults. The study characterises the faulty transformer behaviour under varying conditions of load, supplying voltage, location of the fault, and fault severity and size. Useful characteristic signatures associated with interturn faults extracted from the transformer behaviour under varying fault and transformer operating conditions are expected to yield insights into developing reliable and sensitive fault detection and localisation methods in power transformers.

Fault detection for stator inter-turn short circuit in doubly fed induction generators using adaptive observers

This paper presents an observer-based fault detection approach for inter-turn stator short circuit fault in doubly fed induction generators (DFIG). A state-space model is developed, which can represent both the healthy and faulty conditions. Based on this model, two different adaptive observers: asymptotic adaptive observer and exponential adaptive observer are constructed, which are both able to estimate the fraction of short circuited winding online. The case when the rotor speed is immeasurable is also considered in this paper. The proposed exponential adaptive observer is reformulated into a new observer, which is capable of estimating the fault level and the rotor speed simultaneously. The simulation results demonstrate that this approach has effectiveness on fault detection under both steady state operation and transient state operation. It has strong robustness against the speed and load variations. This advantage has significance on fault detection of wind turbine DFIGs, since the DFIGs are predominantly under varying speed operation.

Modelling and Fault Diagnosis of Stator Inter-Turn Short Circuit in Doubly Fed Induction Generators

AbstractIn this paper, a dynamic state space model of a DFIG with stator inter-turn short circuit fault is proposed. This model can quantitatively describe the fault at any level in any single phase. Both healthy and faulty conditions can be simulated by using this model. Base on this model, an observer based fault detection and diagnosis (FDD) scheme is developed, which can not only provide a rapid detection when fault occurs but also give an accurate diagnosis of the fault position and level. In order to ensure the observer stability under a wide rang of the speed variation of DFIGs, a time varying Kalman like gain supplied with measured rotor speed is applied to the observers. Moreover, an exponential adaptive observer is employed to provide a desirable estimation of fault level. The simulation results demonstrate the effectiveness of this approach in detecting and diagnosing the faults under both stationary and speed varying operations. The latter is particularly important for fault detection of wind turbine DFIGs.

An Online Universal Diagnosis Procedure Using Two External Flux Sensors Applied to the AC Electrical Rotating Machines

This paper presents an original non-invasive procedure for the diagnosis of electromagnetic devices, as well as AC electrical rotating machines using two external flux coil sensors that measure the external magnetic field in the machines’ vicinity. The diagnosis exploits the signal delivered by the two sensors placed in particular positions. Contrary to classical methods using only one sensor, the presented method does not require any knowledge of a presumed machine’s healthy former state. On the other hand, the loading operating is not a disturbing factor but it is used to the fault discrimination. In order to present this procedure, an internal stator inter-turn short-circuit fault is considered as well.

Investigation on Stator Circulating Current Characteristics of Turbogenerators under Air Gap Eccentricity and Rotor Short-circuit Composite Faults

This article investigates the characteristics of the stator winding parallel branches circulating the current of turbogenerators under air gap eccentricity and rotor inter-turn short-circuit composite faults. The presented current characteristics are achieved from the theoretical derivation of the electromotive force difference between two parallel branches and are verified by the experimental results. Three types of the composite faults are studied. The theoretical analysis and the experimental results show the significant difference of the circulating current characteristics between a normal condition and a composite fault condition. An effective diagnosis method for the composite faults based on the circulating current increment is proposed in the end. The investigation results of this article will be beneficial to the composite fault diagnosis of turbogenerators.