1. 1 Neural Network Data Fusion and Uncertainty Analysis for Wind Speed Measurement using Ultrasonic Transducer Juan M. Mauricio Villanueva jmauricio12@gmail.com January, 2011

2. 2 Introduction  There is the need for the determination of the wind power density (WPD), which is used in eolic energy as requirements on wind turbine localization. where:  is the air density and is the wind speed

3. 3 Introduction  The objective of the measurement procedure is to defined a criteria to ensure that the data:  Sufficient quantity To determine the power and quality performance characteristic of the wind turbine accurately

4. 4 Introduction  The wind speed measurement should be supplemented with an estimate of the uncertainty of the measurement  The uncertainty estimate is based on the ISO guide: “Guide to the expression of uncertainty in measurement”

5. 5 Objetives  The purpose of this paper are:  Provide a procedure that will ensure consistency, accuracy and reproducibility into the wind speed measurement  A data fusion procedure based on neural network algorithm to determine the fusion ToF  Assessment the fusion uncertainty of a conventional ultrasonic transducer configuration

6. 6 Wind Speed Measurement Transducers Configuration

7. 7 Measurement Model and Data Fusion Procedures

8. 8  The model is linear in the sense that the model output is a linear combination of its inputs.

9. 9 Measurement and Uncertainty of ToF  Analysis and assessment of uncertainty for ToF measurement through the TH and PD techniques are carried out.  The ToF measurement by TH techniques and m=10 ToF measurement by PD techniques

10. 10 Measurement and Uncertainty of ToF  Uncertainty in measurement is a parameter associated with the result of a measurement.  Following the ISO Guide, the uncertainties are expressed as standard deviations and are denoted standard uncertainties: where: uTh and uPD are the standard deviation values of the TH and PD techniques and uFusion is the standard deviation value of fusion

11. 11 Results and Simulations  We apply the data fusion procedure for the estimation of the ToF, combining independent information of the ToF obtained by the methods of TH and PD  From these results, we can determine the measurements and their associated uncertainties

12. 12 Results and Simulations  The model is simulated in Simulink (MATLAB) Wind speed from 5 to 30 m/s One ToF estimation measurement by TH m=10 ToF estimation measurement by PD Transducers operating frequency: f = 40 kHz; Maximum voltage level: vm = 1 volt; Attenuation medium: Att = 10 % of vm; Additive uncertainty: uA equal to 0.01 volt; Frequency clock: fs = 50 MHz. uTH = 0.5 µs uPD = 0.1 µs

13. 13 Results and Simulations  ToF simulation values and uncertainties (in us) (m/s) ToF Theory (µs)ToF fusion (µs)u fusion (µs) 5239.57239.590.115 10237.86237.850.133 15236.16236.140.137 20234.49234.480.142 25232.85232.830.049 30231.22230.450.052

14. 14 Results and Simulations  From this results, we can make a Gaussian Distribution of ToF measurement fusion.  For example, to the wind speed measurement 10 m/s:

15. 15 Results and Simulations  Gaussian Distribution of ToF measurement fusion.

16. 16 Conclusions  This paper presents a method to wind speed measurement based on neural network for multisensor fusion.  Quantitatively, the fusion procedures increase the accuracy of inference, i.e. reduce the uncertainties in the ToF estimation.  Qualitatively, the neural network fusion procedure take the advantages of the TH and PD techniques when used individually.  The fusion algorithm produces a ToF results with less uncertainty, reducing ambiguity and increasing the reliability of measurement and, consequently, improving the operational performance of the measurement model.