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Neural Network Data Fusion and Uncertainty Analysis for Wind Speed Measurement using
Ultrasonic Transducer
Juan M. Mauricio Villanueva
jmauricio12@gmail.com
January, 2011
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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
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2WPD
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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
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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”
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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
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Wind Speed Measurement Transducers Configuration
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cos M EAB AB
LC
t t
20.74 KC T
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Measurement Model and Data Fusion Procedures
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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.
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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
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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
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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
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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
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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
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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
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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:
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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)
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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.