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1 Neural Network Data Fusion and Uncertainty Analysis for Wind Speed Measurement using Ultrasonic Transducer Juan M. Mauricio Villanueva [email protected] January, 2011

Neural Network Data Fusion and Uncertainty Analysis for Wind Speed Measurement using

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Neural Network Data Fusion and Uncertainty Analysis for Wind Speed Measurement using Ultrasonic Transducer. Juan M. Mauricio Villanueva [email protected]. January, 2011. Introduction. - PowerPoint PPT Presentation

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Page 1: Neural Network Data Fusion and Uncertainty Analysis  for Wind Speed Measurement using

1

Neural Network Data Fusion and Uncertainty Analysis for Wind Speed Measurement using

Ultrasonic Transducer

Juan M. Mauricio Villanueva

[email protected]

January, 2011

Page 2: Neural Network Data Fusion and Uncertainty Analysis  for Wind Speed Measurement using

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

2WPD

Page 3: Neural Network Data Fusion and Uncertainty Analysis  for Wind Speed Measurement using

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

3DPV 31

2DPV

Page 4: Neural Network Data Fusion and Uncertainty Analysis  for Wind Speed Measurement using

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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”Guide to the expression of uncertainty in measurement”

Page 5: Neural Network Data Fusion and Uncertainty Analysis  for Wind Speed Measurement using

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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

Page 6: Neural Network Data Fusion and Uncertainty Analysis  for Wind Speed Measurement using

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Wind Speed Measurement Transducers Configuration

1

cos M EAB AB

LC

t t

20.74 KC T

Page 7: Neural Network Data Fusion and Uncertainty Analysis  for Wind Speed Measurement using

7

Measurement Model and Data Fusion Procedures

Page 8: Neural Network Data Fusion and Uncertainty Analysis  for Wind Speed Measurement using

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Measurement Model and Data Fusion Procedures

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

1

m

i ii

y w x

1 2( ) [ ( ), ( ),..., ( )]Tmx n x n x n x n

1 2( ) [ ( ), ( ),..., ( )]Tmw n w n w n w n

( ) ( ) ( )Ty n x n w n

( 1) ( ) ( )[ ( ) ( ) ( )]w n w n y n x n y n w n

Page 9: Neural Network Data Fusion and Uncertainty Analysis  for Wind Speed Measurement using

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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

1 2 1 10...fusion TH PD m PDToF wToF w ToF w ToF

Page 10: Neural Network Data Fusion and Uncertainty Analysis  for Wind Speed Measurement using

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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

22 2

2

1 10

...fusion fusion fusionfusion TH PD PD

TH PD PD

ToF ToF ToFu u u u

ToF ToF ToF

2 2 221 2 1 11 10...TH PD PDu w u w u w u

Page 11: Neural Network Data Fusion and Uncertainty Analysis  for Wind Speed Measurement using

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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

Page 12: Neural Network Data Fusion and Uncertainty Analysis  for Wind Speed Measurement using

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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

Page 13: Neural Network Data Fusion and Uncertainty Analysis  for Wind Speed Measurement using

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Results and Simulations

ToF simulation values and uncertainties (in us)

(m/s) ToFTheory (µs) ToFfusion (µs) ufusion (µs)

5 239.57 239.59 0.115

10 237.86 237.85 0.133

15 236.16 236.14 0.137

20 234.49 234.48 0.142

25 232.85 232.83 0.049

30 231.22 230.45 0.052

Page 14: Neural Network Data Fusion and Uncertainty Analysis  for Wind Speed Measurement using

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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:

Page 15: Neural Network Data Fusion and Uncertainty Analysis  for Wind Speed Measurement using

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Results and Simulations

Gaussian Distribution of ToF measurement fusion.

237.5 237.6 237.7 237.8 237.9 238 238.1 238.2 238.3 238.40

50

100

150

200

250

300

350

ToFfusion

(s)

Page 16: Neural Network Data Fusion and Uncertainty Analysis  for Wind Speed Measurement using

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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.