The Experts below are selected from a list of 1753137 Experts worldwide ranked by ideXlab platform

Luciano Castillo - One of the best experts on this subject based on the ideXlab platform.

  • a multivariate and multimodal wind Distribution Model
    Renewable Energy, 2013
    Co-Authors: Jie Zhang, Souma Chowdhury, Achille Messac, Luciano Castillo
    Abstract:

    This paper presents a new methodology to accurately characterize and predict the annual variation of wind conditions. The estimate of the Distribution of wind conditions is necessary to quantify the available energy (power density) at a site, and to design optimal wind farm configurations. A smooth multivariate wind Distribution Model is developed to capture the coupled variation of wind speed, wind direction, and air density. The wind Distribution Model developed in this paper avoids the limiting assumption of unimodality of the Distribution. This method, which we call the Multivariate and Multimodal Wind Distribution (MMWD) Model, is an evolution from existing wind Distribution Modeling techniques. Multivariate kernel density estimation, a standard non-parametric approach to estimate the probability density function of random variables, is adopted for this purpose. The MMWD technique is successfully applied to Model (i) the Distribution of wind speed (univariate); (ii) the joint Distribution of wind speed and wind direction (bivariate); and (iii) the joint Distribution of wind speed, wind direction, and air density (multivariate). The latter is a novel contribution of this paper, while the former offers opportunities for validation. Both onshore and offshore wind Distributions are estimated using the MMWD Model. Recorded wind data, obtained from the North Dakota Agricultural Weather Network (NDAWN) and the National Data Buoy Center (NDBC), is used in this paper. The coupled Distribution was found to be multimodal. A strong correlation among the wind condition parameters was also observed.

  • multivariate and multimodal wind Distribution Model based on kernel density estimation
    ASME 2011 5th International Conference on Energy Sustainability Parts A B and C, 2011
    Co-Authors: Jie Zhang, Souma Chowdhury, Achille Messac, Luciano Castillo
    Abstract:

    This paper presents a new method to accurately characterize and predict the annual variation of wind conditions. Estimation of the Distribution of wind conditions is necessary (i) to quantify the available energy (power density) at a site, and (ii) to design optimal wind farm configurations. We develop a smooth multivariate wind Distribution Model that captures the coupled variation of wind speed, wind direction, and air density. The wind Distribution Model developed in this paper also avoids the limiting assumption of unimodality of the Distribution. This method, which we call the Multivariate and Multimodal Wind Distribution (MMWD) Model, is an evolution from existing wind Distribution Modeling techniques. Multivariate kernel density estimation , a standard non-parametric approach to estimate the probability density function of random variables, is adopted for this purpose. The MMWD technique is successfully applied to Model (i) the Distribution of wind speed (univariate); (ii) the Distribution of wind speed and wind direction (bivariate); and (iii) the Distribution of wind speed, wind direction, and air density (multivariate). The latter is a novel contribution of this paper, while the former offers opportunities for validation. Ten-year recorded wind data, obtained from the North Dakota Agricultural Weather Network (NDAWN), is used in this paper. We found the coupled Distribution to be multimodal. A strong correlation among the wind condition parameters was also observed.Copyright © 2011 by ASME

Nurulkamal Masseran - One of the best experts on this subject based on the ideXlab platform.

  • integrated approach for the determination of an accurate wind speed Distribution Model
    Energy Conversion and Management, 2018
    Co-Authors: Nurulkamal Masseran
    Abstract:

    Abstract The Distribution Model of wind-speed data is critical for the assessment of wind-energy potential because it reduces uncertainties in the estimation of wind power output. Thus, an accurate Distribution Model for describing wind-speed data should be determined before a detailed analysis of energy potential is conducted. In this study, information from several goodness-of-fit criteria, e.g., the R2 coefficient, Kolmogorov–Smirnov statistic, Akaike’s information criterion, and deviation in skewness/kurtosis were integrated for the conclusive selection of the best-fit Distribution Model of wind-speed data. The proposed approach integrates standardized scores and subjects each criterion to multiplicative aggregation. The approach was applied in a case study to fit eight statistical Distributions to hourly wind-speed data collected at two stations in Malaysia. The results showed that the proposed approach provides a good basis for the selection of the optimal wind-speed Distribution Model. Furthermore, graphical representations agreed with the analytical results.

She Shens - One of the best experts on this subject based on the ideXlab platform.

  • research on wind speed Distribution Model of wind farm based on its dynamic space time relation
    Power system technology, 2014
    Co-Authors: She Shens
    Abstract:

    Dynamic spatial positions of power generating units in a wind farm are calculated by the speed and direction measured by anemometer tower, and based on the mathematical Model synthesizing wake effect with time-lag effect of wind power generating unit that is constructed on Matlab platform, a Model describing the Distribution of wind speed on each wind power unit is built. Taking an actual wind farm as research object and inputting the measured data by anemometer tower into the built Model, the wind speed at each wind power generating unit can be calculated. The impacts of wake effect and time-lag effect on wind speed Model and power output of wind farm are analyzed, and analysis results show that the wake effect decreases the power output of wind farm and the time-lag effect increases it. Finally, the wind speed Distribution Model is transformed into the power output Model of the whole wind farm and the simulation results are compared with the data measured by anemometer, the effectiveness of the built wind speed Distribution Model of wind farm is validated.

Magnus Nyden - One of the best experts on this subject based on the ideXlab platform.

  • the gamma Distribution Model for pulsed field gradient nmr studies of molecular weight Distributions of polymers
    Journal of Magnetic Resonance, 2012
    Co-Authors: Magnus Roding, Daniel Topgaard, Diana Bernin, Jenny Jonasson, Aila Sarkka, Mats Rudemo, Magnus Nyden
    Abstract:

    Self-diffusion in polymer solutions studied with pulsed-field gradient nuclear magnetic resonance (PFG NMR) is typically based either on a single self-diffusion coefficient, or a log-normal Distribution of self-diffusion coefficients, or in some cases mixtures of these. Experimental data on polyethylene glycol (PEG) solutions and simulations were used to compare a Model based on a gamma Distribution of self-diffusion coefficients to more established Models such as the single exponential, the stretched exponential, and the log-normal Distribution Model with regard to performance and consistency. Even though the gamma Distribution is very similar to the log-normal Distribution, its NMR signal attenuation can be written in a closed form and therefore opens up for increased computational speed. Estimates of the mean self-diffusion coefficient, the spread, and the polydispersity index that were obtained using the gamma Model were in excellent agreement with estimates obtained using the log-normal Model. Furthermore, we demonstrate that the gamma Distribution is by far superior to the log-normal, and comparable to the two other Models, in terms of computational speed. This effect is particularly striking for multi-component signal attenuation. Additionally, the gamma Distribution as well as the log-normal Distribution incorporates explicitly a physically plausible Model for polydispersity and spread, in contrast to the single exponential and the stretched exponential. Therefore, the gamma Distribution Model should be preferred in many experimental situations.

Jie Zhang - One of the best experts on this subject based on the ideXlab platform.

  • a multivariate and multimodal wind Distribution Model
    Renewable Energy, 2013
    Co-Authors: Jie Zhang, Souma Chowdhury, Achille Messac, Luciano Castillo
    Abstract:

    This paper presents a new methodology to accurately characterize and predict the annual variation of wind conditions. The estimate of the Distribution of wind conditions is necessary to quantify the available energy (power density) at a site, and to design optimal wind farm configurations. A smooth multivariate wind Distribution Model is developed to capture the coupled variation of wind speed, wind direction, and air density. The wind Distribution Model developed in this paper avoids the limiting assumption of unimodality of the Distribution. This method, which we call the Multivariate and Multimodal Wind Distribution (MMWD) Model, is an evolution from existing wind Distribution Modeling techniques. Multivariate kernel density estimation, a standard non-parametric approach to estimate the probability density function of random variables, is adopted for this purpose. The MMWD technique is successfully applied to Model (i) the Distribution of wind speed (univariate); (ii) the joint Distribution of wind speed and wind direction (bivariate); and (iii) the joint Distribution of wind speed, wind direction, and air density (multivariate). The latter is a novel contribution of this paper, while the former offers opportunities for validation. Both onshore and offshore wind Distributions are estimated using the MMWD Model. Recorded wind data, obtained from the North Dakota Agricultural Weather Network (NDAWN) and the National Data Buoy Center (NDBC), is used in this paper. The coupled Distribution was found to be multimodal. A strong correlation among the wind condition parameters was also observed.

  • multivariate and multimodal wind Distribution Model based on kernel density estimation
    ASME 2011 5th International Conference on Energy Sustainability Parts A B and C, 2011
    Co-Authors: Jie Zhang, Souma Chowdhury, Achille Messac, Luciano Castillo
    Abstract:

    This paper presents a new method to accurately characterize and predict the annual variation of wind conditions. Estimation of the Distribution of wind conditions is necessary (i) to quantify the available energy (power density) at a site, and (ii) to design optimal wind farm configurations. We develop a smooth multivariate wind Distribution Model that captures the coupled variation of wind speed, wind direction, and air density. The wind Distribution Model developed in this paper also avoids the limiting assumption of unimodality of the Distribution. This method, which we call the Multivariate and Multimodal Wind Distribution (MMWD) Model, is an evolution from existing wind Distribution Modeling techniques. Multivariate kernel density estimation , a standard non-parametric approach to estimate the probability density function of random variables, is adopted for this purpose. The MMWD technique is successfully applied to Model (i) the Distribution of wind speed (univariate); (ii) the Distribution of wind speed and wind direction (bivariate); and (iii) the Distribution of wind speed, wind direction, and air density (multivariate). The latter is a novel contribution of this paper, while the former offers opportunities for validation. Ten-year recorded wind data, obtained from the North Dakota Agricultural Weather Network (NDAWN), is used in this paper. We found the coupled Distribution to be multimodal. A strong correlation among the wind condition parameters was also observed.Copyright © 2011 by ASME