Showing papers by "Vincent Choqueuse published in 2010"

Blind Recognition of Linear Space–Time Block Codes: A Likelihood-Based Approach

Abstract: Blind recognition of communication parameters is a research topic of high importance for both military and civilian communication systems. Numerous studies about carrier frequency estimation, modulation recognition as well as channel identification are available in literature. This paper deals with the blind recognition of the space-time block coding (STBC) scheme used in multiple-input-multiple-output (MIMO) communication systems. Assuming there is perfect synchronization at the receiver side, this paper proposes three maximum-likelihood (ML)-based approaches for STBC classification: the optimal classifier, the second-order statistic (SOS) classifier, and the code parameter (CP) classifier. While the optimal and the SOS approaches require ideal conditions, the CP classifier is well suited for the blind context where the communication parameters are unknown at the receiver side. Our simulations show that this blind classifier is more easily implemented and yields better performance than those available in literature.

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

Abstract: 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.

Advanced signal processing techniques for fault detection and diagnosis in a wind turbine induction generator drive train: A comparative study

Abstract: This paper deals with the diagnosis of Wind Turbines based on generator current analysis. It provides a comparative study between traditional signal processing methods, such as periodograms, with more sophisticated approaches. Performances of these techniques are assessed through simulation experiments and compared for several types of fault, including air-gap eccentricities, broken rotor bars and bearing damages.

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

Abstract: 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.

Condition monitoring of wind turbines based on amplitude demodulation

Abstract: 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.