Cost-effective condition monitoring for wind turbines

Cost-effective wind turbine (WT) condition monitoring assumes more importance as turbine sizes increase and they are placed in more remote locations, for example, offshore. Conventional condition monitoring techniques, such as vibration, lubrication oil, and generator current signal analysis, require the deployment of a variety of sensors and computationally intensive analysis techniques. This paper describes a WT condition monitoring technique that uses the generator output power and rotational speed to derive a fault detection signal. The detection algorithm uses a continuous-wavelet-transform-based adaptive filter to track the energy in the prescribed time-varying fault-related frequency bands in the power signal. The central frequency of the filter is controlled by the generator speed, and the filter bandwidth is adapted to the speed fluctuation. Using this technique, fault features can be extracted, with low calculation times, from direct- or indirect-drive fixed- or variable-speed WTs. The proposed technique has been validated experimentally on a WT drive train test rig. A synchronous or induction generator was successively installed on the test rig, and both mechanical and electrical fault like perturbations were successfully detected when applied to the test rig

Performance Analysis of an EEMD-based Hilbert Huang Transform as a Bearing Failure Detector in Wind Turbines

International audienceSustainability and viability of wind farms are highly dependent on the reduction of 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 degeneration of the generator health, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper deals then with the assessment of a demodulation technique for bearing failure detection through wind turbines generator stator current. The proposed technique is based on a modified version of the Hilbert Huang transform. In this version, the use of the EEMD algorithm allows overcoming the well-known mixed mode

Effect of power converter on condition monitoring and fault detection for wind turbine

This paper investigates the impact of power electronics converter when attempting wind turbine condition monitoring system and fault diagnosis by the analysis of fault signatures in the electrical output of the turbine. A wind turbine model has been implemented in the MATLAB/Simulink environment. Fault signature analysis for electrical signals is presented. A signal processing algorithm based on a fast fourier transform is then used to potentially identify fault signatures. The results obtained with this model are validated with experimental data measured from a physical test rig. Through comparison between simulation data and experimental data it is concluded that the power converter has significantly reduced fault signatures from the electrical signal though not entirely extinguished them. It may still be possible to extract some fault information after the converter though this is much more challenging than upstream. Further work is needed to see whether it may be possible to modify the power converter particularly the filter design and the switching elements to avoid removing fault signatures from electrical signals without adding significant cost or compromising performance

Integration of multidimensional fault diagnostic indicators on the example of rolling element bearings

Diagnostics of rotating machinery relies on examining of many dozens of fault indicators that enable recognition of malfunction symptoms at the earliest stage possible. Unfortunately, in many industrial applications and especially in large machinery parks, the number of diagnostic features to monitor goes beyond the perception capabilities of responsible maintenance personnel. Therefore, there is need for a data reduction techniques that simplify and provide the most important information within the condition monitoring system, starting from a single kinematic element. In this paper it is proposed to employ a simple Euclidean distance that relates the object’s condition to the difference between the vibration-based indicators and the initial state. As an example, the authors examine the integration of diagnostic features used to identify localized and extended fault of rolling element bearings for simulated data and real industrial event that occurred at wind turbine’s generator bearing

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

Effect of power converter on condition monitoring and fault detection for wind turbine

This paper investigates the impact of power electronics converter when attempting wind turbine condition monitoring system and fault diagnosis by the analysis of fault signatures in the electrical output of the turbine. A wind turbine model has been implemented in the MATLAB/Simulink environment. Fault signature analysis for electrical signals is presented. A signal processing algorithm based on a fast fourier transform is then used to potentially identify fault signatures. The results obtained with this model are validated with experimental data measured from a physical test rig. Through comparison between simulation data and experimental data it is concluded that the power converter has significantly reduced fault signatures from the electrical signal though not entirely extinguished them. It may still be possible to extract some fault information after the converter though this is much more challenging than upstream. Further work is needed to see whether it may be possible to modify the power converter particularly the filter design and the switching elements to avoid removing fault signatures from electrical signals without adding significant cost or compromising performance

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

Evaluating the challenges associated with the long-term reliable operation of industrial wind turbine gearboxes

Wind turbine gearboxes are required to operate under adverse operational conditions over a long service lifetime. Unfortunately, gearbox designers are yet to achieve the reliability anticipated by wind turbine manufacturers and operators. The poor understanding of variable loading conditions has resulted in the majority of wind turbine gearboxes being unable to reach their expected service lifetime of 20-25 years. This has led to an increasing need to investigate the fundamental issues associated with the degradation of wind turbine gearbox materials during operation in order to improve existing designs and optimise future ones. This paper investigates the various challenges that need to be addressed in order to achieve a noteworthy increase in the operational service lifetime of large-scale industrial wind turbine gearboxes

EEMD-based windturbinebearingfailuredetectionusing the generatorstatorcurrenthomopolarcomponent

International audienceFailure 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 as a tool for failure detection in wind turbine generators for stationary and non stationary cases

Hilbert Transform-Based Bearing Failure Detection in DFIG-Based Wind Turbines

International audienceCost-effective, predictive and proactive maintenance of wind turbines assumes more importance with the increasing number of installed wind farms in more remote location (offshore). A well-known method for assessing impeding problems is to use current sensors installed within the wind turbine generator. This paper describes then an approach based on the generator stator current data collection and attempts to highlight the use of the Hilbert transform for failure detection in a doubly-fed induction generator-based. Indeed, this generator is commonly used in modern variable-speed wind turbines. The proposed failure detection technique has been validated experimentally regarding bearing failures. Indeed, a large fraction of wind turbine downtime is due to bearing failures, particularly in the generator and gearbox