Determination of Optimal Parametric Distribution and Technical Evaluation of Wind Resource Characteristics for Wind Power Potential at Jhimpir, Pakistan

Muhammad Armoghan Khan; Yao Zhang; Jianxue Wang; Jingdong Wei; Muhammad Ali Raza; Aitizaz Ahmad; Yiping Yuan

doi:10.1109/ACCESS.2021.3078511

IEEE Access (Jan 2021)

Determination of Optimal Parametric Distribution and Technical Evaluation of Wind Resource Characteristics for Wind Power Potential at Jhimpir, Pakistan

Muhammad Armoghan Khan,
Yao Zhang,
Jianxue Wang,
Jingdong Wei,
Muhammad Ali Raza,
Aitizaz Ahmad,
Yiping Yuan

Affiliations

Muhammad Armoghan Khan: ORCiD; Shaanxi Key Laboratory of Smart Grid, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China
Yao Zhang: ORCiD; Shaanxi Key Laboratory of Smart Grid, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China
Jianxue Wang: ORCiD; Shaanxi Key Laboratory of Smart Grid, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China
Jingdong Wei: Shaanxi Key Laboratory of Smart Grid, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China
Muhammad Ali Raza: Department Electrical and Computer Engineering, COMSATS University Islamabad at Lahore, Lahore, Pakistan
Aitizaz Ahmad: ORCiD; School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China
Yiping Yuan: Shaanxi Key Laboratory of Smart Grid, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China

DOI: https://doi.org/10.1109/ACCESS.2021.3078511
Journal volume & issue: Vol. 9
pp. 70118 – 70141

Abstract

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The objective of this research work is to analyze wind characteristics and to assess wind power potential by selecting the best fit probability distribution function of Jhimpir Sindh Pakistan. This type of detailed investigation helps wind power generation companies in selecting suitable wind turbine and provides information of wind characteristics of potential site. Eight probability distribution functions are tested on the wind speed data from January 2015 to July 2018. Frequency bins of Weibull and Rayleigh distribution with maximum probabilities of 0.1210 and 0.1143 are most closest representation of our data. In order to, quantitatively analysis which distribution function is best fitting the local wind regime, we have applied the coefficient-of-determination, Kolmogorov-Smirnov, Chi square, Cramer-von Mises, Anderson-Darling tests along with Akaike information and Bayesian information criterion. These statistical test are used to rank the empirical distribution functions in order to identify two distribution function better fitting the actual wind speed data. After selecting two best fitted distribution functions, we analyze wind power potential and compare the error of wind power density based on these distribution functions (Weibull and Rayleigh). The power densities reported varied from 73.67 to 648.73W/m2. Results indicate that power densities of Weibull and Rayleigh for the candidate site are 84.67–698.65W/m2 and 83.67–1021.4W/m2, respectively. The highest error for Weibull and Rayleigh are 0.1850 and 0.5745, respectively. Whereas lowest error are 0.0178 and 0.0180, respectively. Complete analysis suggested that Weibull distribution function is the most suitable for Jhimpir Sindh Pakistan and the studied site is suitable for wind power production. In addition, comprehensive analysis of wind direction at the candidate site suggested that Eastern and Southeastern wind directions are predominant with 38.52% and 33.24% of the total time.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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