Technology in icing on blades
Technology in icing on blades
Turbine technology is the key to any wind power project, in terms of efficiency, risk and supply/demand dynamics. Technical advice is needed to ensure that the choice of turbine has been adequately investigated, especially as the market is increasingly demanding different types of technology due to new projects in cold climates. Projects in cold climate site are new for many developers and currently there are not enough historical data to evaluate the turbines that are made for these conditions.
The accretion of icing on objects is very complex. There are two different ways of modeling icing, the physical accretion process and the meteorological environment that rules the input to the models (S. M. Fikke, Kristjánsson, & Kringlebotn Nygaard, 2008).
The most relevant weather parameters are clouds, wind trajectories, stability, precipitation, topographical influence and turbulence (S. M. Fikke et al., 2008). There will always be data which is less representative, and in these cases the engineers have to use operating experience, inspection and “gut feeling”.
The most common models used in predict icing is the Makkonen model:
Where dM/dt is the icing rate in a standard cylindrical icing collector (defined by ISO 12494 as a cylinder of 1 m length and 30 mm diameter), w is the liquid water content, and A is the collision area of the exposed object. V is the wind speed and α1, α2 and α3 represents collision efficiency, sticking efficiency and accretion efficiency. The collision efficiency is a function of mass, velocity and drag force (Davis, Souza, Joseph, & Verdult, 2015). The Makkonen model uses an empirical function which is based on cylindrical object and small diameter (Davis et al., 2015). This model is also used on WRF model data.
It is possible to use modern high-resolution 3D atmosphere models
Figure 1 The relationship between chord length and rime icing on wind turbines (Davis et al., 2015)
The ice accretion measuring can be performed by use of direct measurement, indirect measurement or numerical modeling.
Direct measurement may be conducted by changing physical properties like mass, reflective properties electrical or thermal conductivity, dielectric coefficient and inductance.
Indirect methods are based on detecting weather conditions that lead to icing: humidity, temperature and wind speed or by detecting the effects of icing.
Empirical or deterministic models are used to determine when icing occurs to evaluate the liquid water content (LWC) and median volume diameter (MVD).
The financial prospect is very important and a project’s cost efficiency depends on the available wind energy during the icing period and on the severity of icing. This analysis requires knowledge about meteorological conditions leading to ice accretion and the turbine’s geometry and operating conditions. The meteorological parameters used for icing prediction are mainly liquid water content (LWC), water droplet diameter (MVD), pressure, temperature and the horizontal distribution of the variables. This kind of measurement is expensive and difficult to conduct. Quantitative data is not always readily available and most of the estimation is empirical (Parent & Ilinca, 2011). It is also important that the measurement is done at the same height as the top blade tip.
Ice mass measurement uses an ice collector that consists of a 30 mm diameter cylinder (Parent & Ilinca, 2011). This method of measurement is good, but there are always some uncertainties. Other sensors using different approaches, such as longitudinal wire waves, vibrating probes or optics exist, but are only used during the operational phase of the turbine. These technologies are expensive and demand high energy (Parent & Ilinca, 2011).
Double anemometry and vane
The use of equipment for measurement masts with one properly heated and one unheated anemometer to estimate wind resource measurements, is cheap and advisable (Parent & Ilinca, 2011). This gives a fairly good idea of the time that ice can affect the turbines. The disadvantages of this method are that it has poor measurement in the tip of the blades, where there is more icing. The other disadvantage is that low temperatures were found to cause negative errors which did not result from icing between heated and unheated anemometers (Parent & Ilinca, 2011). The method is optimal at relatively mild temperatures.
Relative humidity and dew point
Relative humidity is high during in-cloud icing, and the detection of high humidity over 95 % combined with temperature below 0 degree is used to detect icing. In practice air temperature is at frost point nearly all the time when in-cloud icing occurs, and a dew point detector that has been designed for subzero operation could provide valuable information for this situation (Laakso et al., 2005). The first measurement of relative humidity is more used then the dew point measurement. However, this method does not detect icing events during the same period as ice detectors. When the humidity is more than 95-98 % with temperatures of less than 0 degrees, the predictability of icing events using conditions of relative humidity is weak (Parent & Ilinca, 2011).
Visibility and cloud base
When the temperature is below 0 degrees with a minimum wind speed of 2 m/s, in- cloud icing may occur. To classify clouds, the qualitative quotes or visibility distance to estimate the LWC are used. These have a direct effect on the intensity of the in-cloud icing (Parent & Ilinca, 2011). To measure this airport observation, a pyranometer, video monitoring or automatic sensors are used. Another alternative is to create an ice map. Airport observation provides cloud base heights and a cloudiness index based on the observation of the cloud density, on a scale from 1 to 8. When the index is higher than 6/8 and the cloud base height is lower than the wind turbine, icing is detected or the index can be used as a ratio for accretion intensity (Tallhaug, 2003). In Europe this map has been introduced, and may provide the predicted number of icing days with respect to the location. There has also been found that the severity of rime ice is strongly related to terrain roughness (Parent & Ilinca, 2011). This methods overestimates icing, if there has been input of wrong wind speed and temperature for the location. There have also been comments about the reliability of this method at 200 m above the ground (Parent & Ilinca, 2011). Therefore this formula can only provide a rough estimation of the predicted amount of rime accretion.
The pyronometer measures the solar radiation intensity and Dr. H Dobesch concluded that the solar radiation has an effect on the ice map (Parent & Ilinca, 2011). Also, it is very difficult to get accurate data because the radiation network is very sparse and the use of analytical models is quite uncertain for the time span of one to several hours during the day. At higher latitudes the solar radiation intensity is too weak to enhance significant melting processes at low temperatures. Different wind turbines react different to icing, therefore icing maps cannot be interpreted as exact and must be used in combination with local topographical information and measurement statistics (Parent & Ilinca, 2011).
In regional weather predictions, physical mesoscale models (MM5, MC2 and other) may be used to predict upcoming icing events or to describe the likelihood of such events for specific projects or time frames (Parent & Ilinca, 2011). For models that provide information about amount and the rate of icing, there has been used more sophisticated empirical and statistical models. These models consider parameters such as temperature (air, object, wet-bulb and dew point), wind direction, wind speed, cloud height, cloud cover, the humidity profile, precipitation, regional topography, local topography, object size, shape and material composite and solar radiation (Parent & Ilinca, 2011).
Visual detection using video filming of guy wires during icing events. Icefall due to wire vibration has to be accounted for in the analysis. Ice accumulation models are in reasonable agreement to the ice thickness observed on guy wires by an onsite web camera. Improvements may be had by using onsite temperature and wind speed measurements or water droplet density information from a combined analysis of on-site visibility records and cloud base observation from the airport (Harstveit et al., 2005). To detect freezing rain a rain detector with a temperature sensor may be used.
Anti-icing and de-icing technology
Anti-icing and de-icing are strategies for icing mitigation systems. These systems can be divided in passive and active methods. Passive methods such as ice repellant (ice-phobic) coatings on the blades is use to eliminate or prevent ice on the blade surface(Luo, Vidal, & Acho, 2014). Active methods make use of external systems and require a supply of thermal, chemical or pneumatic energy (Parent & Ilinca, 2011).
Anti-icing system prevents ice from accreting on the object. There are different passive methods used in anti-icing systems. Most manufacturers use epoxy or polyester matrix composites reinforced with glass and/or carbon fibers, because of their lower cost compare to the other alternatives (Parent & Ilinca, 2011). Black paint is also a passive method. It involves painting the blades black, which allows them to heat during daylight and is a solution used together with an ice-phobic coating. Chemicals are also an option to avoid the water to turn into ice on the blades by using chemicals to lower the water’s freezing point.
One of the methods for active anti-icing systems is thermal, where heating resistance and warm air can be used in anti-icing mode to prevent icing. Air layer is another method, which consists of an air flow originating inside the blade that is pushed though rows of small holes near the blade’s leading and trailing edges in order to generate a layer of clean and heated air directly around the blade surface. The last active method uses microwaves to heat the blade’s material to prevent ice formation.
De-icing removes the ice layer from the surface. There are different passive methods, and one of them is use of a flexible blade, which is flexible enough to crack the ice loose. Another method is active pitching and semi-active method, which use start/stop cycles to orient iced blades into the sun. For an active de-icing system heating through resistance is a good option. This method consists of electrical heating elements that are embedded inside the membrane or laminated on the surface. The idea is to create a water film between the ice and the surface, and the centrifugal force will throw the ice away. Warm air and radiator is also an active method, and it consists of blowing warm air into the rotor blade at standstill with special tubes. The heat is transferred through the blade shell in order to keep the blade free from ice. The ”flexible pneumatic boots” method inflates the blade with compressed air in order to break ice (Parent & Ilinca, 2011). The inflation cycles last for a few seconds to achieve optimal ice shed and prevent additional ice formation in the inflated surface. After the ice is cracked, it is removed through centrifugal and aerodynamic forces as the turbine turns. One of the last two active de-icing methods is electro impulsive/expulsive method. This method consists of rapid electromagnetically induced vibration pulses in cycles that flex a metal abrasion shield and crack the ice (Parent & Ilinca, 2011). The other one is microwave de-icing. The technology consists of carbon Nanoparticles in a coasting or film that absorb MW radiation and generate heat. The idea is to have microwave generators inside the blades (Johansson et al., 2015).
 “Automated airport weather stations are automated sensor suites which are designed to serve aviation and meteorological observing needs for safe and efficient aviation operations, weather forecasting and climatology.” (Wikipedia)
 Maps indicating light icing or icing in the studied area.