Data Fusion Methods based on Fuzzy Theory for Wind Speed Measurement using
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I2MTC 2008 - IEEE International Instrumentation and Measurement Technology Conference Victoria, Vancouver Island, Canada, May 12-15, 2008
Data Fusion Methods based on Fuzzy Theory for Wind Speed Measurement using Ultrasonic Transducers
Abstract - In this work, a wind speed measurement model based on fuzzy data fusion of the time-of-flight (ToF) information is presented. This information is obtained through threshold detection (TH) and phase difference (PD) techniques. Fuzzy membership functions are derived from ToF measurement values and represent measured values and their uncertainties. Two data fusion methods are presented based on the compatibility relationship between elements to be combined and using weights defined by the OWA (Order Weighted Average) operator. Uncertainty analysis the TH and PD techniques is carried out by determining the uncertainty associated to the ToF measurement. ToF data fusion values are determined considering several measured values using the TH and PD techniques. Keywords - Wind speed measurement, ultrasonic transducers, timeof-flight, uncertainties, datafusion andfuzzy set theory.
2Universidade Federal do Maranhao, Departamento de engenharia de Eletricidade, Sao Luis - MA, Brazil
'{juanmv, ricardo}gele.puc-rio.br, 2catundagdee.ufma.br
The category type B refers to the evaluation of components of uncertainties based on a priori knowledge, previous measurements, datasheets and technical specifications of instruments, calibration documents, etc. This type of uncertainty is often given under the form of intervals corresponding to particular levels of confidence. Therefore, to represent measurement uncertainties one can use interval (or uncertainty) calculus based on fuzzy set theory. This approach is a good alternative to the conventional assessment of uncertainty, while remaining compatible with the ISO Guide. In the fuzzy representation, the best estimation is the value given by defuzzyfication operator. Moreover it provides the intervals for any level of confidence [3-5]. With the purpose of reducing the uncertainties associated to the measurement processes, data fusion approaches are used on instrumentation and measurement. This will increase the confidence with which we can give results of measurements. The commonly used methods for the combination of information - from quantitative data (numeric or fuzzy subset) - are based on algorithms that attribute weights to the available information. In this work, a model for wind speed measurement using ultrasonic transducers is presented, where the ToF measurement procedure is based on fuzzy data fusion approach. Therefore, the measurement and uncertainties of the information obtained from the TH and PD techniques are represented using fuzzy numbers. Two data fusion procedures, the Compatibility Relationships and the Ordered Weighted Average (OWA) are compared with respect to the ToF results, in order to determine the data fusion method that provides the smaller uncertainty.
II. BACKGROUND DEFINITIONS
A usual configuration for measuring wind speed ultrasonic transducers is to line up the transmitting and the receiving transducers with a specific angle to the wind direction, as shown in Figure 1. For the development of the measurement model, the PD and the TH sented and a study of uncertainties propagation is carried out considering the transmission of an ultrasonic sinusoidal wave affected by additive and multiplicative noise. Moreover, the measurement and its uncertainties representation using fuzzy set theory are presented.
Juan M. Mauricio Villanueva 2, Sebastian Y.C. Catunda2, Ricardo Tanscheit' 'Pontificia Universidade Catolica do Rio de Janeiro, Departamento de Engenharia Eletrica, Rio de Janeiro - RJ, Brazil
I. INTRODUCTION Ultrasonic methods are widely used to measure flow velocity in industrial and scientific applications. For example, the wind speed measurement for the determination of wind power density for electrical energy generation, which requires low uncertainty measurement [1]. The operating principle of these applications is principally based on the measurement of the time of flight (ToF) of ultrasonic waves, between two ultrasonic transducers. For this purpose, commonly, two straightforward methods are used to measure the ToF, based on threshold detection technique (TH) and phase difference technique (PD). The uncertainties associated to the process of ToF measurement are usually due to [2, 3]: approximations in the measurement estimation, limited knowledge of the environment: variability of the influences in the measurement such as attenuation of medium, random noise and reflections, and the signal-to-noise ratio (SNR) of the receiving signal. In order to quantify uncertainties, we use the ISO Guide, which has proposed a formal evaluation process for uncertainties, which are classified in agreement with the method of uncertainties assessment, in type A and type B [4]. The category type A refers to the application of statistical processing of measurements results in physical experiments, repeated under similar conditions, where the central tendency and dispersion are very defined and they can be estimated by considering only the first two moments (mean value and variance) of the probability distribution.
Data Fusion Methods based on Fuzzy Theory for Wind Speed Measurement using Ultrasonic Transducers
Abstract - In this work, a wind speed measurement model based on fuzzy data fusion of the time-of-flight (ToF) information is presented. This information is obtained through threshold detection (TH) and phase difference (PD) techniques. Fuzzy membership functions are derived from ToF measurement values and represent measured values and their uncertainties. Two data fusion methods are presented based on the compatibility relationship between elements to be combined and using weights defined by the OWA (Order Weighted Average) operator. Uncertainty analysis the TH and PD techniques is carried out by determining the uncertainty associated to the ToF measurement. ToF data fusion values are determined considering several measured values using the TH and PD techniques. Keywords - Wind speed measurement, ultrasonic transducers, timeof-flight, uncertainties, datafusion andfuzzy set theory.
2Universidade Federal do Maranhao, Departamento de engenharia de Eletricidade, Sao Luis - MA, Brazil
'{juanmv, ricardo}gele.puc-rio.br, 2catundagdee.ufma.br
The category type B refers to the evaluation of components of uncertainties based on a priori knowledge, previous measurements, datasheets and technical specifications of instruments, calibration documents, etc. This type of uncertainty is often given under the form of intervals corresponding to particular levels of confidence. Therefore, to represent measurement uncertainties one can use interval (or uncertainty) calculus based on fuzzy set theory. This approach is a good alternative to the conventional assessment of uncertainty, while remaining compatible with the ISO Guide. In the fuzzy representation, the best estimation is the value given by defuzzyfication operator. Moreover it provides the intervals for any level of confidence [3-5]. With the purpose of reducing the uncertainties associated to the measurement processes, data fusion approaches are used on instrumentation and measurement. This will increase the confidence with which we can give results of measurements. The commonly used methods for the combination of information - from quantitative data (numeric or fuzzy subset) - are based on algorithms that attribute weights to the available information. In this work, a model for wind speed measurement using ultrasonic transducers is presented, where the ToF measurement procedure is based on fuzzy data fusion approach. Therefore, the measurement and uncertainties of the information obtained from the TH and PD techniques are represented using fuzzy numbers. Two data fusion procedures, the Compatibility Relationships and the Ordered Weighted Average (OWA) are compared with respect to the ToF results, in order to determine the data fusion method that provides the smaller uncertainty.
II. BACKGROUND DEFINITIONS
A usual configuration for measuring wind speed ultrasonic transducers is to line up the transmitting and the receiving transducers with a specific angle to the wind direction, as shown in Figure 1. For the development of the measurement model, the PD and the TH sented and a study of uncertainties propagation is carried out considering the transmission of an ultrasonic sinusoidal wave affected by additive and multiplicative noise. Moreover, the measurement and its uncertainties representation using fuzzy set theory are presented.
Juan M. Mauricio Villanueva 2, Sebastian Y.C. Catunda2, Ricardo Tanscheit' 'Pontificia Universidade Catolica do Rio de Janeiro, Departamento de Engenharia Eletrica, Rio de Janeiro - RJ, Brazil
I. INTRODUCTION Ultrasonic methods are widely used to measure flow velocity in industrial and scientific applications. For example, the wind speed measurement for the determination of wind power density for electrical energy generation, which requires low uncertainty measurement [1]. The operating principle of these applications is principally based on the measurement of the time of flight (ToF) of ultrasonic waves, between two ultrasonic transducers. For this purpose, commonly, two straightforward methods are used to measure the ToF, based on threshold detection technique (TH) and phase difference technique (PD). The uncertainties associated to the process of ToF measurement are usually due to [2, 3]: approximations in the measurement estimation, limited knowledge of the environment: variability of the influences in the measurement such as attenuation of medium, random noise and reflections, and the signal-to-noise ratio (SNR) of the receiving signal. In order to quantify uncertainties, we use the ISO Guide, which has proposed a formal evaluation process for uncertainties, which are classified in agreement with the method of uncertainties assessment, in type A and type B [4]. The category type A refers to the application of statistical processing of measurements results in physical experiments, repeated under similar conditions, where the central tendency and dispersion are very defined and they can be estimated by considering only the first two moments (mean value and variance) of the probability distribution.
