Condition monitoring wind turbine gearboxes using on-line/in-line oil analysis techniques

Paper examining condition monitoring wind turbine gearboxes using on-line/in-line oil analysis techniques

On sensor fusion for airborne wind energy systems

A study on filtering aspects of airborne wind energy generators is presented. This class of renewable energy systems aims to convert the aerodynamic forces generated by tethered wings, flying in closed paths transverse to the wind flow, into electricity. The accurate reconstruction of the wing's position, velocity and heading is of fundamental importance for the automatic control of these kinds of systems. The difficulty of the estimation problem arises from the nonlinear dynamics, wide speed range, large accelerations and fast changes of direction that the wing experiences during operation. It is shown that the overall nonlinear system has a specific structure allowing its partitioning into sub-systems, hence leading to a series of simpler filtering problems. Different sensor setups are then considered, and the related sensor fusion algorithms are presented. The results of experimental tests carried out with a small-scale prototype and wings of different sizes are discussed. The designed filtering algorithms rely purely on kinematic laws, hence they are independent from features like wing area, aerodynamic efficiency, mass, etc. Therefore, the presented results are representative also of systems with larger size and different wing design, different number of tethers and/or rigid wings.Comment: This manuscript is a preprint of a paper accepted for publication on the IEEE Transactions on Control Systems Technology and is subject to IEEE Copyright. The copy of record is available at IEEEXplore library: http://ieeexplore.ieee.org

Detailed state of the art review for the different on-line/in-line oil analysis techniques in context of wind turbine gearboxes

The main driver behind developing advanced condition monitoring (CM) systems for the wind energy industry is the delivery of improved asset management regarding the operation and maintenance of the gearbox and other wind turbine components and systems. Current gearbox CM systems mainly detect faults by identifying ferrous materials, water, and air within oil by changes in certain properties such as electrical fields. In order to detect oil degradation and identify particles, more advanced devices are required to allow a better maintenance regime to be established. Current technologies available specifically for this purpose include Fourier transform infrared (FTIR) spectroscopy and ferrography. There are also several technologies that have not yet been or have been recently applied to CM problems. After reviewing the current state of the art, it is recommended that a combination of sensors would be used that analyze different characteristics of the oil. The information individually would not be highly accurate but combined it is fully expected that greater accuracy can be obtained. The technologies that are suitable in terms of cost, size, accuracy, and development are online ferrography, selective fluorescence spectroscopy, scattering measurements, FTIR, photoacoustic spectroscopy, and solid state viscometers

Airflow distortion at instrument sites on the RRS James Clark Ross during the WAGES project

Wind speed measurements obtained from anemometers mounted on ships are prone to systematic errors caused by the distortion of the airflow around the ship's hull and superstructure. This report describes the results of simulations of the airflow around the RRS James Clark Ross made using the computational fluid dynamics (CFD) software VECTIS. The airflow distortion at anemometer sites used during the WAGES project has been quantified at a wind speed of 10 m/s for relative wind directions of 0 (bow-on), 10, 20, 30, 50, 70, 90 and 110 degrees off the bow. The anemometers used in this study were located in the bows of the ship. Temperature sensors were located on the port side of the monkey island. For bow-on flows the anemometers in the bows of the ship experienced relatively small flow distortion. At these sites the flow was decelerated by about 1% of the free stream wind speed. Over the full range of relative wind directions the flow to the R3 sonic is generally accelerated with the largest wind speed biases at flows directly over the beam. The vertical displacement of the airflow increases from around 1 to 2 m for flows directly over the bow, to around 5m for flows over the ships beam as the blockage of the airflow by the ship becomes greater.The airflow distortion at the temperature sensor locations above the monkey island was typically greater than the well-exposed foremast locations. These locations experienced wind speed biases from 6% increase for an airflow directly over the bow, to large decelerations of 55 % when the instruments were in the large recirculation region for flows directly over the starboard side

Experiments and simulations of MEMS thermal sensors for wall shear-stress measurements in aerodynamic control applications

MEMS thermal shear-stress sensors exploit heat-transfer effects to measure the shear stress exerted by an air flow on its solid boundary, and have promising applications in aerodynamic control. Classical theory for conventional, macroscale thermal shear-stress sensors states that the rate of heat removed by the flow from the sensor is proportional to the 1/3-power of the shear stress. However, we have observed that this theory is inconsistent with experimental data from MEMS sensors. This paper seeks to develop an understanding of MEMS thermal shear-stress sensors through a study including both experimental and theoretical investigations. We first obtain experimental data that confirm the inadequacy of the classical theory by wind-tunnel testing of prototype MEMS shear-stress sensors with different dimensions and materials. A theoretical analysis is performed to identify that this inadequacy is due to the lack of a thin thermal boundary layer in the fluid flow at the sensor surface, and then a two-dimensional MEMS shear-stress sensor theory is presented. This theory incorporates important heat-transfer effects that are ignored by the classical theory, and consistently explains the experimental data obtained from prototype MEMS sensors. Moreover, the prototype MEMS sensors are studied with three-dimensional simulations, yielding results that quantitatively agree with experimental data. This work demonstrates that classical assumptions made for conventional thermal devices should be carefully examined for miniature MEMS devices

A Marine Radar Wind Sensor

A new method for retrieving the wind vector from radar-image sequences is presented. This method, called WiRAR, uses a marine X-band radar to analyze the backscatter of the ocean surface in space and time with respect to surface winds. Wind direction is found using wind-induced streaks, which are very well aligned with the mean surface wind direction and have a typical spacing above 50 m. Wind speeds are derived using a neural network by parameterizing the relationship between the wind vector and the normalized radar cross section (NRCS). To improve performance, it is also considered how the NRCS depends on sea state and atmospheric parameters such as air–sea temperature and humidity. Since the signal-to-noise ratio in the radar sequences is directly related to the significant wave height, this ratio is used to obtain sea state parameters. All radar datasets were acquired in the German Bight of the North Sea from the research platform FINO-I, which provides environmental data such as wind measurements at different heights, sea state, air–sea temperatures, humidity, and other meteorological and oceanographic parameters. The radar-image sequences were recorded by a marine X-band radar installed aboard FINO-I, which operates at grazing incidence and horizontal polarization in transmit and receive. For validation WiRAR is applied to the radar data and compared to the in situ wind measurements from FINO-I. The comparison of wind directions resulted in a correlation coefficient of 0.99 with a standard deviation of 12.8°, and that of wind speeds resulted in a correlation coefficient of 0.99 with a standard deviation of 0.41 m s^−1. In contrast to traditional offshore wind sensors, the retrieval of the wind vector from the NRCS of the ocean surface makes the system independent of the sensors’ motion and installation height as well as the effects due to platform-induced turbulence

A micromachined flow shear-stress sensor based on thermal transfer principles

Microhot-film shear-stress sensors have been developed by using surface micromachining techniques. The sensor consists of a suspended silicon-nitride diaphragm located on top of a vacuum-sealed cavity. A heating and heat-sensing element, made of polycrystalline silicon material, resides on top of the diaphragm. The underlying vacuum cavity greatly reduces conductive heat loss to the substrate and therefore increases the sensitivity of the sensor. Testing of the sensor has been conducted in a wind tunnel under three operation modes-constant current, constant voltage, and constant temperature. Under the constant-temperature mode, a typical shear-stress sensor exhibits a time constant of 72 μs