Vincent Choqueuse

Diagnosis of Three-Phase Electrical Machines Using Multidimensional Demodulation Techniques

This paper deals with the diagnosis of three-phase electrical machines and focuses on failures that lead to stator-current modulation. To detect a failure, we propose a new method based on stator-current demodulation. By exploiting the configuration of three-phase machines, we demonstrate that the demodulation can be efficiently performed with low-complexity multidimensional transforms such as the Concordia transform (CT) or the principal component analysis (PCA). From a practical point of view, we also prove that PCA-based demodulation is more attractive than CT. After demodulation, we propose two statistical criteria aiming at measuring the failure severity from the demodulated signals. Simulations and experimental results highlight the good performance of the proposed approach for condition monitoring.

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

Induction 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 to monitor 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.

Wind turbines condition monitoring and fault diagnosis using generator current amplitude demodulation

Wind energy conversion systems have become a focal point in the research of renewable energy sources. In order to make wind turbines as competitive as the classical electric power stations, it is important to reduce the operational and maintenance costs. The most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early detection of the degradation of the generator health, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper provides then an approach based on the generator stator current data collection and attempts to highlight the use of Hilbert transformation for failure detection in a Doubly-Fed Induction Generator (DFIG) based wind turbine for stationary and nonstationary cases.

A comparative study of time-frequency representations for fault detection in wind turbine

To reduce the cost of wind energy, minimization and prediction of maintenance operations in wind turbine is of key importance. In variable speed turbine generator, advanced signal processing tools are required to detect and diagnose the generator faults from the stator current. To detect a fault in non-stationary conditions, previous studies have investigated the use of time-frequency techniques such as the Spectrogram, the Wavelet transform, the Wigner-Ville representation and the Hilbert-Huang transform. In this paper, these techniques are presented and compared for broken-rotor bar detection in squirrel-cage generators. The comparison is based on several criteria such as the computational complexity, the readability of the representation and the easiness of interpretation.

Condition monitoring of wind turbines based on amplitude demodulation

Wind energy conversion systems (WECS) have become a focal point in the research of renewable energy sources. In order to make wind turbine reliable and competitive, it is important to reduce the operational and maintenance costs. The most efficient way to reduce it relies on condition monitoring and fault diagnostics. This paper proposes a new fault detector based on the amplitude demodulation of the three-phase stator current. Simulations show that this low-complexity method is well suited for stationary or non-stationary behavior.

Induction machine fault detection enhancement using a stator current high resolution spectrum

Fault detection in squirrel cage induction machines based on stator current spectrum has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. In this paper, a modified version of MUSIC algorithm has been developed based on the faults characteristic frequencies. This method has been used to estimate the stator current spectrum. Then, an amplitude estimator has been proposed and a fault indicator has been derived for fault severity measurement. Simulated stator current data issued from a coupled electromagnetic circuits approach has been used to prove the appropriateness of the method for air gap eccentricity and broken rotor bars faults detection.

Bearing fault detection in DFIG-based wind turbines using the first Intrinsic Mode Function

Wind energy conversion systems have become a focal point in the research of renewable energy sources. In order to make the DFIG-based wind turbines so competitive as the classical electric power stations it is important to reduce the operational and maintenance costs by continuously monitoring the condition of these systems. This paper provides a method for bearing fault detection in DFIG-based wind turbines. The proposed method uses the first Intrinsic Mode Function (IMF) of the stator current signal. After extracting the first IMF, amplitude-demodulation is performed to reveal a generator bearing fault. Experimental results show that the proposed method significantly improves the result of classical amplitude-demodulation techniques for failure detection.

Induction Machines Fault Detection Based on Subspace Spectral Estimation

The main objective of this paper is to detect faults in induction machines using a condition monitoring architecture based on stator current measurements. Two types of fault are considered: bearing and broken rotor bars faults. The proposed architecture is based on high-resolution spectral analysis techniques also known as subspace techniques. These frequency estimation techniques allow to separate frequency components including frequencies close to the fundamental one. These frequencies correspond to fault sensitive frequencies. Once frequencies are estimated, their corresponding amplitudes are obtained by using the least squares estimator. Then, a fault severity criterion is derived from the amplitude estimates. The proposed methods were tested using experimental stator current signals issued from two induction motors with the considered faults. The experimental results show that the proposed architecture has the ability to efficiently and cost-effectively detect faults and identify their severity.

Estimation of amplitude, phase and unbalance parameters in three phase systems: analytical solutions, efficient implementation and performance analysis

This paper focuses on the estimation of the instantaneous amplitude, phase, and unbalance parameters in three-phase power systems. Due to the particular structure of three-phase systems, we demonstrate that the maximum-likelihood estimates (MLEs) of the unknown parameters have simple closed-form expressions and can be easily implemented without matrix algebra libraries. We also derive and analyze the Cramer-Rao Bounds (CRBs) for the considered estimation problem. The performance of the proposed approach is evaluated using synthetic signals compliant with the IEEE Standard C37.118. Simulation results show that the proposed estimators outperform other techniques and reach the CRB under certain conditions.

Wind turbine bearing failure detection using generator stator current homopolar component ensemble empirical mode decomposition

Failure detection has always been a demanding task in the electrical machines community; it has become more challenging in wind energy conversion systems because sustainability and viability of wind farms are highly dependent on the reduction of the operational and maintenance costs. Indeed the most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early detection of the generator health degeneration, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper provides then an assessment of a failure detection techniques based on the homopolar component of the generator stator current and attempts to highlight the use of the Ensemble Empirical Mode Decomposition (EEMD) as a tool for failure detection in wind turbine generators for stationary and non stationary cases.