Spectral Analysis Of Stator Current Research Articles

Wind turbine doubly-fed induction generator defects diagnosis under voltage dips

Voltage dips are brief but frequent disturbances that can be defined as the most important problem affecting all electrical grids, it cannot be completely avoided and represent the main priority for the industry. This study remains part of monitoring the quality of electrical energy’s field. Its aim is to apply two approaches signals for improving the failure recognition in wind turbine conversion chain. One is based on the combination of spectral analysis of stator current by fast Fourier transform and Lissajous curves technique of grid currents. The second approach is based on the stator current analysis by discrete wavelet transform which is considered an ideal tool for this purpose due to its time-frequency localization. These defects diagnosis methods have shown their efficiency in detection, visualization and analysis of voltage dips in order to develop a control strategy for a wind turbine requiring continuity of its service despite the presence of voltage dips.

Stator Current Square Analysis to Discriminate Between Eccentricity and Demagnetization Faults in PMSMs

The stator current spectral analysis (MCSA) technique can detect certain signatures indicating the presence of faults. The presence of the eccentricity and demagnetization fault in Permanent Magnet Synchronous Machine (PMSM) is manifested by the creation of harmonics of the same frequency, which complicates the task of detecting and identifying these faults. Hence, it is needed to propose and develop separation techniques. The objective of this paper is to study the contribution and the effectiveness of a new diagnostic approach to discriminate between the fault of demagnetization of the permanent magnets and the fault of eccentricity in the PMSM. This approach is based on tracking the signs of the real and imaginary components of the Fourier transform of the square of one of the three stator currents. Indeed, the spectrum of the square of the harmonics of the line current has carried more information related to the demagnetization fault and the eccentricity fault. These two faults are manifested by the creation of harmonics whose amplitude is greater than the one of the stator current. In order to demonstrate the effectiveness of this new approach, a series of experimental tests are carried out. The results obtained show the effectiveness of the proposed approach to separate the two treated faults.

Characterization of Fault Signature Due to Combined Air-Gap Eccentricity and Rotor Faults in Induction Motors

An accurate means of non-invasive condition monitoring of the popular industrial drive, three-phase squirrel-cage induction motor, can help to avoid unscheduled maintenance downtime and loss. Faults like air-gap eccentricity can exist even in a newly assembled drive and hence may co-exist with other internal defects. Despite it being a possible situation, the occurrence of simultaneous faults has seldom been studied. Therefore, there is a need for identifying fault signatures of combined fault conditions in a non-invasive manner. This paper presents a detailed model-based study on a three-phase squirrel-cage induction motor with the simultaneous existence of broken rotor-bar and air-gap mixed eccentricity faults using spectral analysis of stator current, instantaneous power, and estimated air-gap torque signals. The modelling of the machine is done using the Multiple Coupled Circuit method and modified to model the presence of the combined fault conditions. A comparative evaluation with various fault conditions and their severity is carried out by spectral analysis, and unique slip-dependent frequency components are identified in the spectra of diagnostic signals. This fault characterization is the most significant contribution of this paper.

Extension of Winding Function Theory for Modeling and Diagnosis of Partial Demagnetization Fault in PMSM Drive

This paper proposes the development of a new mathematical model dedicated to the diagnostic of the partial Demagnetization Fault (DF) in Permanent Magnet Synchronous Motor (PMSM). To do this, an extension of the Modified Winding Function Approach (MWFA) is proposed to accurately take into account the air gap asymmetry caused by the Demagnetization Fault (DF) in the PMSM inductances calculation. The calculated inductances are then used in the magnetically coupled electrical circuit model to simulate the PMSM behavior in the different operating modes. Finally, a diagnostic method based on the stator current spectral analysis is used to highlight the PMSM stator current spectral content in the various operating modes (with and without Demagnetization Fault).

Discrimination between demagnetization and eccentricity faults in PMSMs using real and imaginary components of stator current spectral analysis

Permanent magnet demagnetization and rotor eccentricity are the main faults in permanents magnet synchronous motors (PMSMs). To detect these faults, motor current signature analysis (MCSA) has become the most common method in the field due to its simplicity, low computation time, and remote monitoring capability. Unfortunately, this method has a major drawback relating to the frequency signatures of demagnetization and eccentricity faults, which appear in the same frequency locations. To avoid this drawback while retaining the main advantages of the MCSA method, this paper proposes a new approach for discrimination between demagnetization and eccentricity faults in PMSMs. The proposed approach is based on the simultaneous monitoring of the real and imaginary components of the characteristic harmonics of these faults, which are obtained from stator current spectral analysis. This monitoring is based on the evolution of the characteristic harmonic signs in these two components. The positive or negative signs, make it possible to accurately discriminate between the effects produced by eccentricity faults from the effects produced by demagnetization faults. Finally, several experiments have been presented in this paper to demonstrate the capability and effectiveness of the proposed approach.

An Automatic Diagnosis of an Inverter IGBT Open-Circuit Fault Based on HHT-ANN

The main objective of this paper is to propose a method that contributes to the automatic diagnosis of the IGBT open-circuit fault of an inverter for detecting and localizing the fault using the stator current spectral analysis technique. The proposal focusses on the use of the combination of signal processing and artificial intelligence techniques for the detection and localization of the fault. The proposed diagnosis method begins first by using the Hilbert-Huang transform (HHT) to detect the harmonic characterizing the fault based on the complete empirical ensemble mode decomposition (CEEMD) of the three-stator currents (ias, ibs, ics ). The CEEMD provides the intrinsic mode function (IMF) which contains information of the IGBT open-circuit fault. For the exact choice of the IMF, a statistical study based on the calculation of the root mean square values (RMS) is carried out for each IMF. The IMF choice depends on the condition that the RMS values of the inverter upper IGBTs are always lower than the RMS values of the complementary ones. The results obtained can be seen to respond well to the RMS condition and the spectral envelope of the IMF1 makes it possible to detect the harmonic characterizing the inverter IGBT open-circuit fault. The proposed diagnosis method then moves to the use of the artificial neural network (ANN) to localize the faulty IGBT. The results obtained using the proposed method are validated experimentally and demonstrate well their effectiveness with a very high classification rate.

Spectral Analysis of Output Voltages and Currents as a Criterion for Technical Diagnostics of Synchronous Generators of Ship Diesel Engine

Abstract Diesel generator sets (DGU) are very widely used in autonomous power supply systems. An example is marine power plants, diesel locomotives with electric power transmission, uninterruptible power supply units, etc. The power of ship DGUs reaches 2–6 or more MW in one unit. The number of units on ships can be different, but, as a rule, at least two. In this paper, we propose a method of monitoring the dynamic electromechanical system “diesel synchronous generator”. The method aims at using a synchronous generator machine as a multifunctional accurate and sensitive sensor for diagnostic parameters of the electromechanical system. The proposed method of technical diagnostics is based on continuous monitoring of non-uniformity of diesel torque, fluctuations in the instantaneous angular velocity and instantaneous angular acceleration of the diesel crankshaft. These data are the results of a spectral analysis of stator currents and voltages of a synchronous generator.

Experimental Bearing Fault Detection, Identification, and Prognosis through Spectral Kurtosis and Envelope Spectral Analysis

Rolling element bearings are machinery elements that are widely used in aerospace and industrial applications to support rotating shafts, thereby reducing mechanical friction and heating. However, these components are subject to several kinds of faults which, in general, can be divided into single-point or localized and distributed faults. A common way to detect ball bearing localized or raceway faults is by identifying and analyzing the so-called characteristic “bearing frequencies.” This article presents an approach to bearing condition-based maintenance which comprises fault detection and identification and condition prognosis, based on spectral kurtosis and squared envelope spectral analysis of stator current. In this work, envelope spectrum analysis, based on spectral kurtosis, is applied to identify these frequencies and to evaluate its applicability as a severity estimation index. To evaluate the performance of this index, experimental tests were carried out considering bearings with outer raceway defects at different stages, simulating different fault severities. The numerical results confirm the applicability of the described methodology for detecting and identifying faults, as well as estimating the generated fault severity levels, suggesting that the proposed fault severity index can be applied for bearing monitoring in real world industrial applications.

Industrial fan installations diagnostics based on spectral analysis of stator currents

Industrial fan installations diagnostics based on spectral analysis of stator currents

Induction Motor Bearing Fault Analysis Using a Root-MUSIC Method

This paper describes a new diagnosis approach, the Root-Multiple Signal Classification (MUSIC) (RM) method, for identification of the progressive cracking in the bearing of induction motors. This approach has several advantages compared with the stator current spectral analysis using the conventional Periodogram method. Indeed, the main advantage of this approach is its very good frequency resolution for a very short acquisition time, something impossible to achieve with the conventional method. However, in order to reduce the computation time, which is the main drawback of the RM method, this method will be applied to only a specified frequency band; one that carries information about the sought fault. Experimental results show the effectiveness of the RM method on the reliability of the incipient bearing fault detection.

Diagnosis of Bearing Faults of Induction Motors by Spectral Analysis of Stator Currents

The article is dedicated to investigate possibility of diagnostics bearing faults of induction motors by stator currents analysis. The main features of stator currents analysis are studied. The possibility of identifying air-gap eccentricity due to the working with damaged bearings by stator currents investigating is revealed. The recommendations of monitoring and configuration of a proper diagnosis of induction motor are provided.

Contribution to the Fault Diagnosis of a Doubly Fed Induction Generator for a Closed-loop Controlled Wind Turbine System Associated with a Two-level Energy Storage System

—In this article, a contribution to the fault diagnosis of a doubly fed induction generator for a closed-loop controlled wind turbine system associated with a two-level energy storage system using an on-line fault diagnostic technique is proposed. This technique is proposed to detect the rotor fault in the doubly fed induction generator under non-stationary conditions based on the spectral analysis of stator currents of the doubly fed induction generator by an adaptive fast Fourier transform algorithm. Furthermore, to prevent system deterioration, a fractional-order controller with a simple design method is used for the control of the whole wind turbine system. The fractional-order controller ensures that the system is stable in both healthy and faulty conditions. Additionally, to improve the production capacity under wind speed fluctuations and grid demand changes, a two-level energy storage system consisting of a supercapacitor bank and lead-acid batteries is proposed. The obtained simulation results show that the objectives of the fault diagnosis procedure and control strategy are reached.

The Motor Rotor Fault Diagnosis Method Based on Linear Mixing Blind Separation Model

The detection precision of fault diagnosis based on frequency spectral analysis of stator current is easily restricted by noise jamming and frequency resolution. A fault diagnosis method for induction motor based on linear mixing model is proposed to resolve this problem. The fault characteristic signals are separated from the motor stator current by Fast-ICA algorithm and its amplitude is calculated according to the estimated mixing matrix. The fault diagnosis is achieved by difference of the amplitude on the normal state and the fault state of the motor. In this paper, the fault diagnosis of the broken rotor bars faults is used as an example to explain the conclusion as mentioned. Experiment result shows that the broken-rotor-bar fault can be diagnosed by the algorithm with better effect on the condition of short data block.

Modeling, control and fault diagnosis of an isolated wind energy conversion system with a self-excited induction generator subject to electrical faults

In this paper, a contribution to modeling and fault diagnosis of rotor and stator faults of a Self-Excited Induction Generator (SEIG) in an Isolated Wind Energy Conversion System (IWECS) is proposed. In order to control the speed of the wind turbine, while basing on the linear model of wind turbine system about a specified operating point, a new Fractional-Order Controller (FOC) with a simple and practical design method is proposed. The FOC ensures the stability of the nonlinear system in both healthy and faulty conditions. Furthermore, in order to detect the stator and rotor faults in the squirrel-cage self-excited induction generator, an on-line fault diagnostic technique based on the spectral analysis of stator currents of the squirrel-cage SEIG by a Fast Fourier Transform (FFT) algorithm is used. Additionally, a generalized model of the squirrel-cage SEIG is developed to simulate both the rotor and stator faults taking iron loss, main flux and cross flux saturation into account. The efficiencies of generalized model, control strategy and diagnostic procedure are illustrated with simulation results.

Diagnostics of an induction-motor rotor by the spectral analysis of stator currents

A method for achieving more efficient revealing and more unambiguous evaluation of various kinds of defects in the induction-motor (IM) rotor, which is based on changes occurring in the characteristic signs of the spectrum of the stator current, is presented. The results and analysis of measurements performed for artificially specified cases of faults in the rotor of a small-capacity IM are presented.

Signal Injection as a Fault Detection Technique

Double frequency tests are used for evaluating stator windings and analyzing the temperature. Likewise, signal injection on induction machines is used on sensorless motor control fields to find out the rotor position. Motor Current Signature Analysis (MCSA), which focuses on the spectral analysis of stator current, is the most widely used method for identifying faults in induction motors. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics. This paper proposes the injection of an additional voltage into the machine being tested at a frequency different from the fundamental one, and then studying the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies.

Rotor Failures Diagnosis of Squirrel Cage Induction Motors with Different Supplying Sources

The growing application and the numerous qualities of induction motors (IM) in industrial processes that require high security and reliability levels has led to the development of multiple methods for early fault detection. However, various faults can occur, such as stator short-circuits and rotor failures. Traditionally the diagnosis machine is done through a sinusoidal power supply, in the present paper we study experimentally the effects of the rotor failures, such as broken rotor bars in function of the ac supplying, the load and show the impact of the converter from diagnosis of the machine. The technique diagnosis used is based on the spectral analysis of stator currents or stator voltages respectively according to the types of induction motor ac supplying. So, four different ac supplying are considered: ? the IM is directly by the balanced three-phase network voltage source, ? the IM is fed by a sinusoidal current source given the controlled by hysteresis, ? the IM is fed (in open loop) by a scalar control imposing through ratio V/f=constant, ? the IM is controlled through a vector control using space vector pulse width modulation (SVPWM) technique inverter with an outer speed loop.

Detection of outer raceway bearing defects in small induction motors using stator current analysis

We investigate the application of induction motor stator current spectral analysis (MCSA) for detection of rolling element bearing damage from the outer raceway. In this work, MCSA and vibration analysis are applied to induction motor to detect outer raceway defects in faulty bearings. Data acquisition, recording, and fast fourier transform (FFT) algorithms are done by using the Lab VIEW programming language. Experimental results verify the relationship between vibration analysis and MCSA, and identify the presence of outer raceway bearing defects in induction machines. This work also indicates that detecting fault frequencies by motor currents is more difficult than detecting them by vibration analysis. The use of intensive resolution FFT is recommended in MCSA for detecting faults easily. Reinstalling a faulty bearing can alter the characteristic frequencies and it is difficult to compare results from different bearings or even from the same bearing in different installations.

Influence of the motor load inertia and torque in the fault diagnosis of rotors in induction machines

During the last decades, non-invasive techniques have been proposed to carry out the fault diagnosis in electric machines. When these techniques are employed in the fault detection of the induction machine's rotors, they exhibit a strong dependence on factors such as the motor load inertia or the opposed torque. In order to develop automatic diagnostic systems or the diagnosis assistance over the rotor state, such as expert systems or knowledge based systems, it is necessary to have available further information to weigh up the influence of these factors. This work presents a study done, based on induction motor's mathematical models, about the incidence of the motor inertia and the opposed torque in some non-invasive fault detection technique employed more frequently. The model takes into account in an independent way each of the rotor bars and then allows to represent different faulty situations. These techniques with non-invasive features do not require sensors directly over the motor and thus allow a diagnosis even on-line when the machine is running. Particularly the study of the following techniques was approached: power spectral analysis, torque spectral analysis, stator current spectral analysis and Park's current vector behaviour. Therefore the diagnostic may be based in the measurement of motor's external variables such as applied voltages and stator currents.