Estimation Spectrale Paramétrique Dédiée au Diagnostic de la Génératrice Asynchrone dans un Contexte Éolien

National audienceLe développement des éoliennes o shores et des hydroliennes implique la nécessité de minimiser et de prévoir les opérations de maintenance. Par conséquent, des techniques de traitement de signal avancées sont requises pour détecter la présence et diagnostiquer une défaillance à partir de mesures vibratoires, acoustiques, ou à travers l'acquisition des courants statoriques. La génératrice asynchrone est largement utilisées dans les systèmes éoliens. Malgré sa robustesse et sa fiabilité, la machine asynchrone est assujettie à des défaillances diverses et variées. L'objectif est donc de les détecter à un stade précoce afin de prévenir d'éventuelles pannes et d'assurer la continuité de la production d'énergie. Cet article s'intéresse donc à la détection des défauts des génératrices asynchrones en se basant sur l'analyse des courants statoriques. Par ailleurs, un schéma de détection et caractérisation des défauts est proposé et ses performances analysées. L'intérêt de cette nouvelle approche est démontré en utilisant des données de simulation issus d'un modèle de la génératrice basé sur les circuits électriques magnétiquement couplés pour la détection des défauts de rupture de barres et d'excentricité mécaniques

Induction Machine Diagnosis using Stator Current Advanced Signal Processing

International audienceInduction machines are widely used in industrial applications. Safety, reliability, efficiency and performance are major concerns that direct the research activities in the field of electrical machines. Even though the induction machines are very reliable, many failures can occur such as bearing faults, air-gap eccentricity and broken rotor bars. Therefore, the challenge is to detect them at an early stage in order to prevent breakdowns. In particular, stator current-based condition monitoring is an extensively investigated field for cost and maintenance savings. In fact, several signal processing techniques for stator current-based induction machine faults detection have been studied. These techniques can be classified into: spectral analysis approaches, demodulation techniques and time-frequency representations. In addition, for diagnostic purposes, more sophisticated techniques are required in order to determine the faulty components. This paper intends to review the spectral analysis techniques and time-frequency representations. These techniques are demonstrated on experimental data issued from a test bed equipped with a 0.75 kW induction machine. Nomenclature O&M = Operation and Maintenance; WTG = Wind Turbine Generator; MMF = Magneto-Motive Force; MCSA = Motor Current signal Analysis; PSD = Power Spectral Density; FFT = Fast Fourier Transform; DFT = Discrete Fourier Transform; MUSIC = MUltiple SIgnal Characterization; ESPRIT = Estimation of Signal Parameters via Rotational Invariance Techniques; SNR = Signal to Noise Ratio; MLE = Maximum Likelihood Estimation; STFT = Short-Time Fourier Transform; CWT = Continuous Wavelet Transform; WVD = Wigner-Ville distribution; HHT = Hilbert-Huang Transform; DWT = Discrete Wavelet Transform; EMD = Empirical Mode Decomposition; IMF = Intrinsic Mode Function; AM = Amplitude Modulation; FM = Frequency Modulation; IA = Instantaneous Amplitude; IF = Instantaneous Frequency; í µí± ! = Supply frequency; í µí± ! = Rotational frequency; í µí± ! = Fault frequency introduced by the modified rotor MMF; í µí± ! = Characteristic vibration frequencies; í µí± !"# = Bearing defects characteristic frequency; í µí± !" = Bearing outer raceway defect characteristic frequency; í µí± !" = Bearing inner raceway defect characteristic frequency; í µí± !" = Bearing balls defect characteristic frequency; í µí± !"" = Eccentricity characteristic frequency; í µí± ! = Number of rotor bars or rotor slots; í µí± = Slip; í µí°¹ ! = Sampling frequency; í µí± = Number of samples; í µí±¤[. ] = Time-window (Hanning, Hamming, etc.); í µí¼ = Time-delay; í µí¼ ! = Variance; ℎ[. ] = Time-window

Non-stationary spectral estimation for wind turbine induction generator faults detection

International audienceDevelopment of large scale offshore wind and marine current turbine farms implies to minimize and predict maintenance operations. In direct- or indirect-drive, fixed- or variable-speed turbine generators, advanced signal processing tools are required to detect and diagnose the generator faults from vibration, acoustic, or generator current signals. The induction generator is traditionally used for wind turbines power generation. Even if induction machines are highly reliable, they are subjected to many types of faults. The aim then, is to detect them at an early stage in order to prevent breakdowns and consequently ensure the continuity of power production. In this context, this paper deals with wind turbines condition monitoring using a fault detection technique based on the generator stator current. The detection algorithm uses a recursive maximum likelihood estimator to track the time-varying fault characteristic frequency and the related energy. Furthermore, a decision-making scheme and a related criterion are proposed. The feasibility of the proposed approach has been demonstrated using simulation data issued from coupled magnetic circuits induction generator model driven by a wind turbine for both electrical asymmetry and mechanical imbalance

Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation

International audienceCondition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA) has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. This paper focuses on the application of MCSA for the detection of abnormal mechanical conditions that may lead to induction machines failure. In fact, this paper is devoted to the detection of single-point defects in bearings based on parametric spectral estimation. A multi-dimensional MUSIC (MD MUSIC) algorithm has been developed for bearing faults detection based on bearing faults characteristic frequencies. This method has been used to estimate the fundamental frequency and the fault related frequency. Then, an amplitude estimator of the fault characteristic frequencies has been proposed and fault indicator has been derived for fault severity measurement. The proposed bearing faults detection approach is assessed using simulated stator currents data, issued from a coupled electromagnetic circuits approach for air-gap eccentricity emulating bearing faults. Then, experimental data are used for validation purposes

On Impedance Spectroscopy Contribution to Failure Diagnosis in Wind Turbine Generators

International audienceWind turbines proliferation in industrial and residential applications is facing the problem of maintenance and fault diagnosis. Periodic maintenances are necessary to ensure an acceptable life span. The aim of this paper is therefore to assess impedance spectroscopy contribution to the failure diagnosis of doubly-fed induction generator-based wind turbines. Indeed, impedance spectroscopy is already used for the diagnosis of batteries, fuel cells, and electrochemical systems. For evaluation purposes, simulations are carried-out on a 9-MW wind farm consisting of six 1.5-MW wind turbines connected to a 25-kV distribution system that exports power to a 120-kV grid. In this context, two common failures are investigated: phase grounding and phase short-circuits. In addition, generator stator resistance variation is also considered for performance evaluation of impedance spectroscopy

Maximum likelihood frequency estimation in smart grid applications

International audienceThis paper focuses on the estimation of the fundamental frequency in balanced three-phase power systems. Specifically, we propose a Maximum Likelihood Estimator (MLE) that exploits the multidimensional nature of electrical signals. For perfectly sinusoidal signals, we show that the MLE can be expressed according to the periodogram of the instantaneous positive component. For harmonic signals, we demonstrate that the MLE can be approximated by a cumulated periodogram of the zero, positive and negative sequence components. As compared to single-phase estimators, statistical analysis and simulation results prove that the proposed estimator decreases the Mean Square Error by a factor of three, whatever the Signal to Noise Ratio (SNR) or data length. Furthermore, simulations with experimental data show that the proposed technique outperforms classical spectral estimators such as MUSIC

A Parametric Spectral Estimator for Faults Detection in Induction Machines

International audienceCurrent spectrum analysis is a proven technique for fault diagnosis in electrical machines. Current spectral estimation is usually performed using classical techniques such as, periodogram (FFT) or its extensions. However, these techniques have several drawbacks since their frequency resolution is limited and additional post-processing algorithms are required to extract a relevant fault detection criterion. Therefore, this paper proposes a new parametric spectral estimator that fully exploits the faults sensitive frequencies. The proposed technique is based on the maximum likelihood estimator and offers high-resolution capabilities. Based on this approach, a fault criterion is derived for detecting several fault types. The proposed faults detection technique is assessed using simulations, issued from a coupled electromagnetic circuits approach-based simulation tool. It is afterwards validated using experiments on a 0.75-kW induction machine test bed for the particular case of bearing faults

Condition Monitoring of Induction Motors Based on Stator Currents Demodulation

International audienceOver the past several decades, induction machine condition monitoring have received increasing attention from researchers and engineers. Several induction machine faults detection techniques have been proposed that are based on vibration, temperature, and currents/power monitoring, etc. Motor current signature analysis is a cost-effective method, which has been widely investigated. Specifically, it has been demonstrated that mechanical and electrical induction machine faults can be effectively diagnosed using stator currents demodulation. Therefore, this paper proposes to investigate the use of demodulation techniques for bearing faults detection and diagnosis based on stator currents analysis. If stator currents are assumed to be mono-component signals, the demodulation techniques include the synchronous demodulator, the Hilbert transform, the Teager energy operator, the Concordia transform, the maximum likelihood approach and the principal component analysis. For a multi-component signal, further preprocessing techniques are required such as the Empirical Mode Decomposition (EMD) or the Ensemble EMD (EEMD). The studied demodulation techniques are demonstrated for bearing faults diagnosis using simulation data, issued from a coupled electromagnetic circuits approach-based simulation tool, and experiments on a 0.75kW induction machine test bed

Induction machine faults detection using stator current parametric spectral estimation

International audienceCurrent spectrum analysis is a proven technique for fault diagnosis in electrical machines. Current spectral estimation is usually performed using classical techniques such as periodogram (FFT) or its extensions. However, these techniques have several drawbacks since their frequency resolution is limited and additional post-processing algorithms are required to extract a relevant fault detection criterion. Therefore, this paper proposes a new parametric spectral estimator that fully exploits the faults sensitive frequencies. The proposed technique is based on the maximum likelihood estimator (MLE) and offers high-resolution capabilities. Based on this approach, a fault criterion is derived for detecting several fault types.The proposed technique is assessed using simulation signals, issued from a coupled electromagnetic circuits approach-based simulation tool for mechanical unbalance and electrical asymmetry faults detection. It is afterward validated using experiments on a 0.75-kW induction machine test bed for the particular case of bearing faults

Current Frequency Spectral Subtraction and Its Contribution to Induction Machines' Bearings Condition Monitoring

International audienceInduction machines are widely used in industrial applications. Safety, reliability, efficiency, and performance are major concerns that direct the research activities in the field of electrical machines. Even though the induction machine is very reliable, many failures can occur such as bearing faults, air-gap eccentricity, and broken rotor bars. The challenge is, therefore, to detect them at an early stage in order to prevent breakdowns. In particular, stator current-based condition monitoring is an extensively investigated field for cost and maintenance savings. In this context, this paper deals with the assessment of a new stator current-based fault detection approach. Indeed, it is proposed tomonitor induction machine bearings by means of stator current spectral subtraction, which is performed using short-time Fourier transform or discrete wavelet transform. In addition, diagnosis index based on the subtraction residue energy is proposed. The proposed bearing faults condition monitoring approach is assessed using simulations, issued from a coupled electromagnetic circuits approach-based simulation tool, and experiments on a 0.75-kW induction machine test bed