The Experts below are selected from a list of 99072 Experts worldwide ranked by ideXlab platform
Luai M Alhems - One of the best experts on this subject based on the ideXlab platform.
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assessment of wind energy potential using wind energy conversion system
Journal of Cleaner Production, 2019Co-Authors: Muhammad Shoaib, I A Siddiqui, Shamim Khan, Shafiqur Rehman, Luai M AlhemsAbstract:Abstract Wind energy, as a renewable resource, is the most rapidly growing source that produces electrical energy using wind turbines. Such a wind energy conversion system is both economical and is environmental friendly. It requires understanding of wind conditions at the site under study. With this intent, wind characteristics of Jhampir (district Thatta Sindh, Pakistan) are investigated and wind energy potential is determined. The study is conducted using 10-min averaged wind speed data obtained from Alternate Energy Development Board of Pakistan for a period of three years (2007–2010). Monthly, seasonal, and yearly analysis is performed by fitting measured wind speed data to a Weibull distribution function. Weibull shape and scale parameters are determined numerically using Maximum Likelihood Method, Modified Maximum Likelihood Method, and Energy Pattern Factor Methods. The suitability of the fit is assessed using goodness-of-fit tests, such as, Root Mean Square Error, Coefficient of Determination (R2), and Chi-Square (χ2) tests. In all three data analysis periods, RMSE values varied between 10−2 and 10−4. Similarly, R2 values varied between 0.989 and 0.996 and χ2-test between 10−4 and 10−8. For entire data set, all the tests showed better performance of Maximum Likelihood and Modified Maximum Likelihood Methods compared to Energy Pattern Factor. In case of monthly analysis, Maximum Likelihood Method performed better compared to Modified Maximum Likelihood Method and Energy Pattern Factor according to root mean square Error and χ2 tests results. Seasonal performance of all the methods is found to be similar with marginal superiority of MLM over other methods. A very good agreement is observed between standard deviation values for measured wind speed data distribution and fitted Weibull distribution using Maximum Likelihood Method estimator. Additionally, to understand the optimum directional efficiency, directional wind power densities are calculated. Finally, a wind turbine is used to the seasonal and yearly wind speed data to determine the actual wind energy potential of the site. Extracted wind energy values for four seasons are found to be 1691, 2851, 4572, and 916 kWh with an annual yield of 10054 kWh. Wind energy values obtained for different periods and directions suggest that Jhampir is a suitable site for developing the wind power plant.
Muhammad Shoaib - One of the best experts on this subject based on the ideXlab platform.
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assessment of wind energy potential using wind energy conversion system
Journal of Cleaner Production, 2019Co-Authors: Muhammad Shoaib, I A Siddiqui, Shamim Khan, Shafiqur Rehman, Luai M AlhemsAbstract:Abstract Wind energy, as a renewable resource, is the most rapidly growing source that produces electrical energy using wind turbines. Such a wind energy conversion system is both economical and is environmental friendly. It requires understanding of wind conditions at the site under study. With this intent, wind characteristics of Jhampir (district Thatta Sindh, Pakistan) are investigated and wind energy potential is determined. The study is conducted using 10-min averaged wind speed data obtained from Alternate Energy Development Board of Pakistan for a period of three years (2007–2010). Monthly, seasonal, and yearly analysis is performed by fitting measured wind speed data to a Weibull distribution function. Weibull shape and scale parameters are determined numerically using Maximum Likelihood Method, Modified Maximum Likelihood Method, and Energy Pattern Factor Methods. The suitability of the fit is assessed using goodness-of-fit tests, such as, Root Mean Square Error, Coefficient of Determination (R2), and Chi-Square (χ2) tests. In all three data analysis periods, RMSE values varied between 10−2 and 10−4. Similarly, R2 values varied between 0.989 and 0.996 and χ2-test between 10−4 and 10−8. For entire data set, all the tests showed better performance of Maximum Likelihood and Modified Maximum Likelihood Methods compared to Energy Pattern Factor. In case of monthly analysis, Maximum Likelihood Method performed better compared to Modified Maximum Likelihood Method and Energy Pattern Factor according to root mean square Error and χ2 tests results. Seasonal performance of all the methods is found to be similar with marginal superiority of MLM over other methods. A very good agreement is observed between standard deviation values for measured wind speed data distribution and fitted Weibull distribution using Maximum Likelihood Method estimator. Additionally, to understand the optimum directional efficiency, directional wind power densities are calculated. Finally, a wind turbine is used to the seasonal and yearly wind speed data to determine the actual wind energy potential of the site. Extracted wind energy values for four seasons are found to be 1691, 2851, 4572, and 916 kWh with an annual yield of 10054 kWh. Wind energy values obtained for different periods and directions suggest that Jhampir is a suitable site for developing the wind power plant.
Dalibor Petkovic - One of the best experts on this subject based on the ideXlab platform.
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adaptive neuro fuzzy approach for estimation of wind speed distribution
International Journal of Electrical Power & Energy Systems, 2015Co-Authors: Dalibor PetkovicAbstract:Abstract Probability distribution of wind speed is very important information needed in the assessment of wind energy potential. For this reason, a large number of studies have been published concerning the use of a variety of probability density functions to describe wind speed frequency distributions. Two parameter Weibull distribution is widely used and accepted method. In this investigation adaptive neuro-fuzzy inference system (ANFIS) was used to predict the probability density distribution of wind speed. The estimation and prediction results of ANFIS model are calculated using three statistical indicators i.e. root means square Error, Coefficient of determination and Pearson Coefficient. The results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. Moreover, the results indicate that proposed ANFIS model can adequately predict the probability distribution of wind speed.
Krishna Mohan Pakkurthi - One of the best experts on this subject based on the ideXlab platform.
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mixture probability distribution functions to model wind speed distributions
international journal of energy and environmental engineering, 2012Co-Authors: Ravindra Kollu, Srinivasa Rao Rayapudi, S V L Narasimham, Krishna Mohan PakkurthiAbstract:Accurate wind speed modeling is critical in estimating wind energy potential for harnessing wind power effectively. The quality of wind speed assessment depends on the capability of chosen probability density function (PDF) to describe the measured wind speed frequency distribution. The objective of this study is to describe (model) wind speed characteristics using three mixture probability density functions Weibull-extreme value distribution (GEV), Weibull-lognormal, and GEV-lognormal which were not tried before. Statistical parameters such as maximum Error in the Kolmogorov-Smirnov test, root mean square Error, Chi-square Error, Coefficient of determination, and power density Error are considered as judgment criteria to assess the fitness of the probability density functions. Results indicate that Weibull-GEV PDF is able to describe unimodal as well as bimodal wind distributions accurately whereas GEV-lognormal PDF is able to describe familiar bell-shaped unimodal distribution well. Results show that mixture probability functions are better alternatives to conventional Weibull, two-component mixture Weibull, gamma, and lognormal PDFs to describe wind speed characteristics.
I A Siddiqui - One of the best experts on this subject based on the ideXlab platform.
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assessment of wind energy potential using wind energy conversion system
Journal of Cleaner Production, 2019Co-Authors: Muhammad Shoaib, I A Siddiqui, Shamim Khan, Shafiqur Rehman, Luai M AlhemsAbstract:Abstract Wind energy, as a renewable resource, is the most rapidly growing source that produces electrical energy using wind turbines. Such a wind energy conversion system is both economical and is environmental friendly. It requires understanding of wind conditions at the site under study. With this intent, wind characteristics of Jhampir (district Thatta Sindh, Pakistan) are investigated and wind energy potential is determined. The study is conducted using 10-min averaged wind speed data obtained from Alternate Energy Development Board of Pakistan for a period of three years (2007–2010). Monthly, seasonal, and yearly analysis is performed by fitting measured wind speed data to a Weibull distribution function. Weibull shape and scale parameters are determined numerically using Maximum Likelihood Method, Modified Maximum Likelihood Method, and Energy Pattern Factor Methods. The suitability of the fit is assessed using goodness-of-fit tests, such as, Root Mean Square Error, Coefficient of Determination (R2), and Chi-Square (χ2) tests. In all three data analysis periods, RMSE values varied between 10−2 and 10−4. Similarly, R2 values varied between 0.989 and 0.996 and χ2-test between 10−4 and 10−8. For entire data set, all the tests showed better performance of Maximum Likelihood and Modified Maximum Likelihood Methods compared to Energy Pattern Factor. In case of monthly analysis, Maximum Likelihood Method performed better compared to Modified Maximum Likelihood Method and Energy Pattern Factor according to root mean square Error and χ2 tests results. Seasonal performance of all the methods is found to be similar with marginal superiority of MLM over other methods. A very good agreement is observed between standard deviation values for measured wind speed data distribution and fitted Weibull distribution using Maximum Likelihood Method estimator. Additionally, to understand the optimum directional efficiency, directional wind power densities are calculated. Finally, a wind turbine is used to the seasonal and yearly wind speed data to determine the actual wind energy potential of the site. Extracted wind energy values for four seasons are found to be 1691, 2851, 4572, and 916 kWh with an annual yield of 10054 kWh. Wind energy values obtained for different periods and directions suggest that Jhampir is a suitable site for developing the wind power plant.