Ice detection using thermal infrared radiometry on wind turbine blades
Introduction
Wind energy is the most competitive renewable energy and the energy source with a more projection nowadays. Wind farms are located in areas with suitable wind characteristics, frequently prone to icing occurrence. Wind farms in these areas present problems related to icing such as energy losses, mechanical failures, downtimes, problems to access for human resources, measurement errors or safety hazards among others (see Fig. 1). The work carried out as part of the research project IcingBlades [1] shows that 18,966 MW h were lost over a period of 29 months as a sole consequence of blades icing up in a set of 517 wind turbines, with a total installed power of 682 MW. This energy lost is practically equivalent to the main stops together, e.g. change of multiplier, turbine change, etc. In Spain, with more than 21,000 MW installed, this phenomenon would be equivalent to a loss more than 550 GW h of power production, i.e. 45 million € every 29 months [1]. These production losses would be equivalent to the energy consumption of 200,000 households and savings of 658,682 tons of CO2. Fig. 1 shows the main causes of the production energy losses, where ice on blades is the principal one. These energy losses involve an increment of the operation and maintenance (O&M) costs.
Onshore wind farms are usually located in elevated areas in order to get the maximum wind velocity [2], [3]. These locations are often exposed to freezing temperatures, i.e. it presents multiple problems due to the icing blades, leading to power generation losses and costs [4], [5]. The WECO (Wind Energy in Cold Climate) project analysed the ice effects, energy generation and icing in wind turbines [6], [7]. It is estimated that 20% of the wind farms are installed in areas with high probability of icing [8].
Parameters such as temperature, wind speed, relative humidity or air density, among others, condition the ice appearance (see Fig. 2). A classification of different types of icing is presented in Ref. [9], discerning between in-cloud icing and precipitation and hoar frost. In-cloud icing appears when the atmospheric temperature is below 0 °C and the humidity is high. Super-cooled water droplets hit the surface of the structure and frozen at the time of impact. The main problem is the accumulation of different layers of this kind of ice.
Frost is the most common cause of ice appearance in wind turbines. It grows in all parts of the wind turbine but the onset occurs in the leading edge of the blades, owing the incident velocity [10].
The main problems due to icing blades are: Power loss by the reduction of aerodynamic efficiency; unbalanced loads on turbines; influence in the lifetime of the components; increased noise generated by blades; changes in blade surfaces by ice accretion; safety hazard and measurement errors [11].
The objective for the ice prevention or removal systems is the reduction of the wind turbine downtimes. The mitigated wind turbines are those with a system to deal with ice accretion. During the icing stage, the ice growth is controlled by the system installed in order to avoid the alarm, reducing the necessary downtimes to remove the ice from the turbine. When the accumulation is significant, the alarm appears and the wind turbine is stopped until the ice is removed. Downtimes are smaller than those in the non-mitigated machines. During post-icing the wind turbine can operate regularly [12].
This industry requires of significant improvements in reliability, lifetime or availability that it is done by an efficient maintenance based on condition monitoring systems [13]. Modern wind turbines need also an autonomous condition monitoring system because of the associated repair costs, especially for off-shore plants, where any repair actions can extend several weeks due to the difficult working conditions [2].
This paper presents a novel approach for operational icing detection based on thermal remote sensing. A thermal infrared sensor is used to measure the radiance emitted by the blade surface. The differences in terms of emissivity between ice and other materials allow the automatic and quick detection of ice formation by analysing the radiance values registered. The effect of dust accumulation in wind turbine has been also considered.
Section snippets
Condition monitoring for icing blades detection
Traditional ice detection uses meteorological equipment that simply measures conditions for icing, but this does not detect ice on blades. It does not give operators enough information to take action such as shutting down the turbine to prevent damage [14], [15].
Wind turbines should have a correct ice detection system to predict when the icing appears on the structure. Measurements are usually done on the nacelle, which generates false alarms when the ice volume varies radially and across the
Thermal infrared radiometer
In this work, an Apogee SI-111 Thermal Infrared Radiometer (IRT) was used (Fig. 3). This broad-band IRT derives the temperature by converting the thermal radiance within the range 8–14 μm, coming from the target defined by the circular field of view (22°), to electrical signals, with a response time of less than one second. The estimation error is 0.2 °C (calibrated temperature range from −30 to 65 °C). An anodized aluminium body and a radiation shield prevent the sensor from temperature changes
Experimental procedure
A section of a real wind turbine blade was used to carry out the experiments. The thermal infrared sensor was used to measure the radiometric temperature of the surface of the white paint of the blade. The observed TR temperature corresponds to the very thin layer on top of the blade. Emissivity of the white paint coverage of the blades does not differ significantly from ice emissivity. However, if a patch of some metallic material with a very low emissivity is used as a reference, a drastic
Laboratory measurements
Temperatures were measured over both an original white painted blade spot and the aluminium foil (Fig. 8).
The emissivity correction is necessary for accurately measuring the surface temperature, in other case the temperature measured at the body surface by the sensor is inaccurate if it is not taking into account the reflected infrared radiation.
The radiation detected by the infrared sensor includes two components, the radiation emitted directly by the body surface, and the radiation reflected
Conclusions
Icing blades is considered one of the main problems for wind turbines. Ice accumulation on the blade surface leads to a reduction of the aerodynamic efficiency and increases the maintenance costs.
This paper reports an operational technique based on thermal remote sensing to detect ice accumulation on the blade surface. This method takes advantage of the different radiative behaviour of surfaces with different emissivity values. Radiometric temperature of an aluminium foil assembled in the blade
Acknowledgments
This work has been supported by the Spanish National project IcingBlades (Ref. IPT-2012-0563-120000) and OptiWindSeaPower (Ref.: DPI2015-67264).
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