Evaluation of statistical analysis methods for detection of partial icing of anemometers. Irene Helmersson Master thesis 2011

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1 Evaluation of statistical analysis methods for detection of partial icing of anemometers. Irene Helmersson Master thesis 2011

2 First some background about icing

3 Definition of icing The physical process of the deposition of super cooled rain or fog droplets and their subsequent freezing on an object. Phase 1: The movement and the attachment of the droplets on the surface. Phase 2: The transformation from fluid form to solid form

4 Phase 1: Determines what fraction of the water content of the air that will be deposited on the surface. The aerodynamic properties of the airflow of the droplet of the object

5 Phase 2: Determines what fraction of the deposited water that can be frozen. The thermo-dynamical characteristics of The impinging droplets The surface of the object The deposited ice that is already on the surface The surrounding atmosphere

6 Super cooled water droplets Clouds consist of small water droplets or ice crystals. Ice crystals can begin to form when the air temperature reaches 0 C. T < 0 C > ice crystals. How come the air contains liquid water at subzero degrees?

7 Ice nuclei For a droplet to start the freezing process it needs an ice nuclide to start the process on Ice nuclei = particles in the atmosphere e. g. AgI (silver ions) The super cooled droplets may or may not freeze depending on whether ice nuclei are present Pure water droplets (in theory) freezing at -40 C Super cooling are not uncommon down to -15 C Super cooled droplets freezes instantly in contact with an object

8 Dry or wet growth during the freezing process Dry growth when flux water droplets < freezing velocity Air is in-bedded in the ice structure Wet growth when flux water droplets > freezing velocity The droplets melts together before freezing

9 Temperature- and wind speed dependence

10 Meteorological factors that affect the density of the ice The ice density increases with increasing liquid water content in the air increasing droplet size increasing wind speed increasing surrounding temperature

11 Evaluation of wind potential Continuity and correctness of the measurements are crucial for the evaluation of a sites wind potential. Wintertime and on high altitudes and/or latitudes the instruments are affected by periods of icing. Undetected partial icing of anemometers leads to under estimation of the sites wind potential

12 Quality assuring of data Icing is today detected manually by wind analysts The difficulty in detecting icing is in periods when the instruments are only partially affected by ice. The main goal of this study is to find an automatic method that detects partial icing of anemometers.

13 Question formulation Can a statistical analysis of measurement data from an anemometer affirm if the instrument has been affected by partial icing during the measurement period?

14 Assumption An increased weight on an anemometers cups will increase the moment of inertia of the cups rotation.

15 Assumption An increased moment of inertia leads to the instrument not being able to register the natural fluctuation of the wind and thereby get a different movement pattern.

16 Evaluation of four statistical variables Distribution of wind velocity 1. Kurtosis, 4th moment 2. Skewness, 3rd moment 3. Difference in change velocity? 4. Change of turbulence intensity measured by the instrument due to icing?

17 Evaluation Distribution of wind velocity 1. Kurtosis, 4th moment Describes how common extreme values are compared to the normal distribution 2. Skewness, 3rd moment Describes where in the velocity range the most common values are.

18 Evaluation 3. Difference in change velocity? The ratio of subsequent values 4. Change of turbulence intensity due to icing? U i U i+1 U i =10min meanwindspeed

19 Validation of icing Experiment An experiment was conducted sampling 1 Hz data during one week in October The data was later analyzed regarded to the statistical variables earlier showed. The same analysis was made on measurements from a conventional mast in the northern part of Sweden.

20 Experimentation Four anemometers U1: Un-modified U2: Modified with sticking clay. U3: Modified with feathers U4: Modified with plastic foam

21 Mast arrangement The experiment was conducted at a field station, 8 km north from Uppsala (Sweden). Four 2 m high masts over a grass covered surface. Measuring period: 22 nd 31 th of October 2010

22 Mean wind difference due to different types of icing

23 Mean wind velocity before and after the change of weather The winds went from calm and low velocities to gusty and higher wind velocities in the end of the week. The low density modified anemometers under estimates the wind velocity throughout the measurement. The high density modified anemometer over estimates the wind speed during the period of gusty winds.

24 Results from the evaluation of the statistical variables

25 Comparing results: Analysis of data from a conventional measuring mast Measuring period: 38 days in October November 2010 Temperature at 60 and 100 m Wind velocity and wind direction at 60, 80 and 100 m

26 Överblick

27 Results from the evaluation of the statistical variables

28 Conclusions Experiment Icing of cup anemometers can lead to both under estimation and over estimation of wind velocity. Only by directly comparing the measurement series one can determine whether one of them are incorrect Obvious difficulty detecting partial icing this may lead to under estimation of sites wind power potential The experiment gave up to 15 % difference in mean wind velocity due to different types of icing

29 Conclusions Evaluation of the statistical variables for detection of icing Added weight or inertia is not shown in the distribution of wind velocity nor by studying the ratio of subsequent values. For identification of icing occurrences there is no useful difference in any of the statistical variables evaluated in this study.

30 Conclusions Measurement of turbulence intensity Results in this study show that the turbulence intensity is underestimated by a few percent by an anemometer with ice load. Hence, the turbulence intensity is not as much describing the characteristics of the atmosphere as it is describing the instruments capacity of detecting small changes in the wind velocity.

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