Inter-turn Fault Detection Research Articles

Winding Turn-to-Turn Faults Detection of Fault-Tolerant Permanent-Magnet Machines Based on a New Parametric Model

This paper proposes a parametric model for inter-turn fault detection in a fault- tolerant permanent-magnet (FTPM) machine, which can predict the effect of the short- circuit fault to various physical quantity of the machine. For different faulty operations, a new effective stator inter-turn fault detection method is proposed. Finally, simulations of vector-controlled FTPM machine drives are given to verify the feasibility of the proposed method, showing that even single-coil short-circuit fault could be exactly detected.

Detection of Synchronous Motor Inter-Turn Faults Based on Spectral Analysis of Park’S Vector / Detekcja Zwarc Zwojowych W Silniku Synchronicznym Bazujaca Na Analizie Spektralnej Wektora Przestrzennego Pradu Twornika

This paper describes detection of synchronous motor inter-turn faults based on symptoms contained in stator phase currents. Armature short circuit, caused by insulation degradation are quite commonly occurring defects in electrical machines. Initially, short circuit comprises mostly single coils, causing the temperature rise due to higher value of current, which can reach up to tens times of the rated value. At the same time the phase current does not increase significantly. Increased temperature leads to rapid damage of the insulation and shorting the adjacent coils spreading the fault to the entire winding in a short time. Thus, it is very important to detected this type of fault in its early stage. Unfortunately currently used motor protection devices are insensitive to short-circuits of a small number of turns, because they cause too small quantitative changes in the phase currents. Phase currents begin to rise to the level detectable by protection devices when a large part of the winding is already covered by a fault. Therefore, there is a need for research on diagnostics of this type of damage. For the purpose of this paper a stepped short circuit fault of one coil group in the stator phase winding is performed. Shorting resistance values are chosen so that the short fault is diagnosed in its early stage. Spectral analysis of stator phase currents is carried out followed by spectral analysis of stator currents Park’s vector. Comparison of the results of both studies shows that the signal of stator current Park’s vector is more suitable in diagnostics of this type of faults.

Single-Turn Fault Detection in Induction Machine Using Complex-Wavelet-Based Method

Interturn short circuit is often confused with voltage imbalance in induction machines. Therefore, detection and classification of single-turn fault (TF) are becoming important in the presence of voltage imbalances, under various loading conditions. Substantial studies are conducted on the interturn fault detection, but a comprehensive method for classifying the faults at different operating points of the machine, under varying supply conditions, is still a challenge. This is a critical problem in industries since the induction motors form the major workhorses. The artificial-intelligence-based techniques are advanced methods in fault monitoring. This, when combined with optimization techniques, is expected to give improved and accurate results with minimum false alarms. In this paper, a technique is developed, based on recent developments in the wavelet-based analysis, particularly in the complex wavelet domain. The support vector machines are adopted for comparing the classification accuracy obtained using complex-wavelet- and standard discrete-wavelet-based methods. The receiver operating characteristic curves indicate that the fault detection, down to single turn, is feasible using a single current sensor.

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.

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.

An experimental approach for investigating low-level interturn winding faults in power transformers

Interturn faults are one of the most prevalent and potentially destructive electrical faults in power transformers. In spite of an extensive interturn fault detection literature, the issue still constitutes an open problem. The difficulty is mainly caused by lacking of enough information on the characteristic signatures associated with interturn faults which were not clearly brought out by the previous studies. This contribution is aimed at obtaining a better understanding of the physical behavior of the power transformers in the presence of interturn faults as well as extracting several features which would be useful to specify the transformers interturn faults. The approach keeps at disposal a 100 kVA distribution transformer on which interturn faults were imposed and an experimental setup consisting of an instrumentation system and suitable transducers which enables monitoring selected characteristics of the transformer. The paper examines the faulted transformer performance under various fault and transformer operating conditions. Unique characteristic fault-signatures derived from the experiments will provide an important basis for developing more reliable and sensitive methods to detect interturn faults on the transformer windings well before such faults lead to a catastrophic failure with serious damage to the windings.

Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors

Online fault diagnosis plays a crucial role in providing the required fault tolerance to drive systems used in safety-critical applications. Short-circuit faults are among the common faults occurring in electrical machines. This paper presents a review of existing techniques available for online stator interturn fault detection and diagnosis (FDD) in electrical machines. Special attention is given to short-circuit-fault diagnosis in permanent-magnet machines, which are fast replacing traditional machines in a wide variety of applications. Recent techniques that use signals analysis, models, or knowledge-based systems for FDD are reviewed in this paper. Motor current is the most commonly analyzed signal for fault diagnosis. Hence, motor current signature analysis is a topic of elaborate discussion in this paper. Additionally, parametric and finite-element models that were designed to simulate interturn-fault conditions are reviewed.

A DWT‐based approach for detection of interturn faults in power transformers

PurposeInterturn winding faults, one of the most important causes of power transformers failures, cannot be detected by existing detection methods until they develop into high‐level faults with more severe damage to the transformer. The purpose of this paper is to describe development of a new discrete wavelet transform (DWT) based approach for detection of winding interturn faults.Design/methodology/approachThe following approach was accomplished for development of the proposed fault detection method in this study. The DWT was first applied to decompose the terminal current signals of a transformer, which in turn were obtained from simulations using a finite elements method model of the transformer, into a series of wavelet components. Based on the characteristic features associated with interturn faults extracted from the decomposed waveforms of the terminal currents, a detection scheme was developed. An experimental setup was used to validate the proposed detection method.FindingsThe results of this study demonstrate the efficacy of DWT applied on terminal currents of the transformer to identify interturn faults on the windings well before such faults lead to a catastrophic failure. It is believed that, based on the present findings, there definitely exists scope for improving interturn fault diagnosis with wavelet transform.Research limitations/implicationsPerforming more detailed studies to find all relevant characteristics of the wavelet transform in this application, identifying the location of the faulted turns along winding, applying the method for indicating early stages of turn insulation deterioration and evaluating other type of wavelets for this application would be some future directions of this research.Practical implicationsWith the proposed method, it is becoming possible to detect early signs of the fault occurrence, so that the necessary corrective actions can be taken to prevent long‐lasting outages and reduce down times of the faulty power transformer. The method will be particularly useful as a complement for the classical protection devices of the power transformers.Originality/valueSome recent studies have been carried out regarding the application of DWT for discrimination between an internal fault and other disturbances such as magnetizing inrush and external faults. This paper extends those studies for the detection of interturn faults using more quantitative and qualitative characteristics features.

Online monitoring of power transformers for detection of internal winding short circuit faults using negative sequence analysis

As a recent trend, online monitoring techniques for electrical machines, mainly including power transformers, has been considered very important. This paper presents a new online method for detection of interturn faults in the power transformers using the ratio of negative sequence components of the primary and secondary line currents. The ratio is equal to the turn ratio during external faults as well as in the supply or load imbalance conditions, while it differs from the turn ratio when interturn winding faults occur. The main feature of the proposed method is its capability to detect low level interturn faults which typically cannot be detected by the traditional transformer protection devices before they developed into high level faults with more severe damage to the power transformer. In this work, a major improvement has been suggested to overcome this problem to a great extent. The proposed method can unambiguously detect interturn faults even down to two shorted turns along the winding. The method is not influenced by the supply and load harmonics even under heavily distorted conditions. The performance of the proposed technique was studied for a variety of operating conditions using data generated by simulations on a finite element model. Copyright © 2010 John Wiley & Sons, Ltd.

Stator inter-turn fault detection of an induction motor using neuro-fuzzy techniques

Stator inter-turn fault detection of an induction motor using neuro-fuzzy techniquesMotivated by the superior performances of neural networks and neuro-fuzzy approaches to fault detection of a single phase induction motor, this paper studies the applicability these two approaches for detection of stator inter-turn faults in a three phase induction motor. Firstly, the paper develops an adaptive neural fuzzy inference system (ANFIS) detection strategy and then compares its performance with that of using a multi layer perceptron neural network (MLP NN) applied to stator inter-turn fault detection of a three phase induction motor. The fault location process is based on the monitoring the three phase shifts between the line current and the phase voltage of the induction machine.

Fast Single-Turn Sensitive Stator Interturn Fault Detection of Induction Machines Based on Positive- and Negative-Sequence Third Harmonic Components of Line Currents

Unambiguous detection of stator interturn faults for induction machines at their incipient stage, i.e., few turns' fault, has recently received great attention. Traditionally, interturn faults are detected using negative-sequence current and impedance. However, their effectiveness under supply imbalance conditions is questionable. Recently, line-current third harmonic (± 3f) has also been used in an attempt to achieve this goal. Nevertheless, issues such as inherent structural asymmetry and voltage imbalance also influence the + 3f. In this paper, the positiveand negative-sequence third harmonics (± 3f) of line current under different operating conditions have been explored by combining space and time harmonics. The suggested fault signature was obtained by removing residual components from tested quantities. Simulation and experimental results using 1 s of data indicate that the proposed ± 3f signatures are capable of very effectively detecting even a single-turn fault, and distinguish it from voltage imbalance and structural asymmetry.

New algorithms for power transformer inter-turn fault protection

In the paper new algorithms for detection of internal inter-turn faults in power transformers are described. Such faults are extremely difficult to detect since they induce negligible increase of the currents at the transformer terminals, although the currents flowing at the fault place are very high and dangerous for the transformer to be protected. The algorithms developed are based on the differential equation of the equivalent circuit of the transformer. In one version additional information from a CT installed inside of the triangle of delta side windings is used, which brings very promising results. Theoretical investigations are supported and illustrated with simulation studies performed both with MATLAB and EMTP-ATP programs.

Stator Interturn Fault Detection of Synchronous Machines Using Field Current and Rotor Search-Coil Voltage Signature Analysis

Our recent observations suggested that harmonics in the field current are very promising to detect stator interturn faults in synchronous machines. So far, an increase in some of the even harmonics in the field current has been reported to detect such faults. However, no explanation has been provided for the cause of these harmonics. Moreover, the even harmonics can significantly increase with supply unbalance as well as time harmonics, which can lead to a serious confusion. Hence, in this study, an in-depth investigation was conducted to determine the origin of various harmonic components in the field current and their feasibility to detect stator faults. It was found that, owing to structural asymmetries of the field winding, some of these components clearly increased with stator interturn fault. The findings are helpful to detect faults involving few turns without ambiguity, in spite of the presence of supply unbalance and time harmonics. Both simulation and experimental results are presented in this paper. The diagnosis results have also been verified using a rotor-mounted search coil, which can also be used to detect even a one-turn stator fault very effectively.

Detection and localization of inter-turn fault in the HV winding of a power transformer using wavelets [Comments and authors' reply

We discuss the paper by M.R. Rao and B.P. Singh (ibid. vol. 8, pp. 652-567, 2001) describing a wavelet approach for identification and localization of minor or small faults within HV transformer windings during lightning impulse tests on them. Although the authors' claim appear to be impressive, there are serious questions regarding the circuit models and computational procedures used, as well as, in the interpretation of the results obtained from the wavelet method. Therefore, the following is intended to obtain clarification from the authors and to help the reader with the usage of wavelets in addressing such problems.

Detection and localization of interturn fault in the HV winding of a power transformer using wavelets

One of the standard tests carried out on a power transformer is the lightning impulse, to corroborate the integrity of its winding insulation. The existing transfer function method used for diagnostics lacks sensitivity in detecting minor internal faults. Hence a new approach using 'wavelet transforms' is developed to analyze the non-stationary neutral current signal which varies due to the existence of a fault, This approach involves the choice of a basis function applied on the neutral current signal and analyzed in the time-frequency domain. The effectiveness of the approach is that the exact instant of fault occurrence is directly known, such that its actual location in the winding can be predicted easily. This paper presents the approach to simulated interturn fault at three selected locations in the partially interleaved HV winding of a 50 MVA, 400/11.5 kV station transformer such that its performance is recognized. Minor faults, such as one turn short, are simulated and successfully detected using this approach. The results are in good conformity with the theoretical calculations based on the length of the conductor involved and the wave propagation velocity.

Novel approach to alternator field winding interturn fault detection

The paper demonstrates how artificial neural networks can be used for the online detection of interturn faults on the rotor of an alternator. A simple network has been designed and tested on a laboratory micromachine. The tests have shown that by employing a neural network to predict the field current from measurements at the stator terminals, operation with a faulted rotor can be successfully identified.