Optimal approach for wind resource assessment using Kolmogorov–Smirnov statistic: A case study for large-scale wind farm in Pakistan. (May 2021)
- Record Type:
- Journal Article
- Title:
- Optimal approach for wind resource assessment using Kolmogorov–Smirnov statistic: A case study for large-scale wind farm in Pakistan. (May 2021)
- Main Title:
- Optimal approach for wind resource assessment using Kolmogorov–Smirnov statistic: A case study for large-scale wind farm in Pakistan
- Authors:
- Saeed, Muhammad Abid
Ahmed, Zahoor
Zhang, Weidong - Abstract:
- Abstract: Weibull distribution has been widely utilized for better understanding, quantification, and optimal utilization of wind energy globally. Although numerical approaches are widely used for Weibull parameters estimation (WPE), their results still lack consistency. In this situation, Artificial Intelligence Optimization Techniques (AIOT) might be a powerful tool to attaining high precision; however, in the case of WPE, standard methods may not ensure convergence. In this work, a generalized mathematical model is derived for AIOT convergence in the case of WPE. Based on the mathematical model Kolmogorov–Smirnov statistic is utilized to establish a convergence optimizer function ( C o p t ) for WPE. The Weibull fitness tests are computed using real-time data from 13 different locations of Pakistan to check the effeteness of C o p t that illustrates that C o p t shows better results than numerical methods. For the economic aspect, Levelized economic cost is carried out for all the targeted sites using ten commercially available wind turbines. It is observed that five of the targeted sites are very promising for wind power production. The study is expected to provide an alternative method for WPE and the deployment of wind energy technology as a future power source in the country. Highlights: A Novel optimal approach for estimating Weibull parameters. Kolmogorov–Smirnov metric is used as a convergence function of AI-optimization. For assessment and comparison, data fromAbstract: Weibull distribution has been widely utilized for better understanding, quantification, and optimal utilization of wind energy globally. Although numerical approaches are widely used for Weibull parameters estimation (WPE), their results still lack consistency. In this situation, Artificial Intelligence Optimization Techniques (AIOT) might be a powerful tool to attaining high precision; however, in the case of WPE, standard methods may not ensure convergence. In this work, a generalized mathematical model is derived for AIOT convergence in the case of WPE. Based on the mathematical model Kolmogorov–Smirnov statistic is utilized to establish a convergence optimizer function ( C o p t ) for WPE. The Weibull fitness tests are computed using real-time data from 13 different locations of Pakistan to check the effeteness of C o p t that illustrates that C o p t shows better results than numerical methods. For the economic aspect, Levelized economic cost is carried out for all the targeted sites using ten commercially available wind turbines. It is observed that five of the targeted sites are very promising for wind power production. The study is expected to provide an alternative method for WPE and the deployment of wind energy technology as a future power source in the country. Highlights: A Novel optimal approach for estimating Weibull parameters. Kolmogorov–Smirnov metric is used as a convergence function of AI-optimization. For assessment and comparison, data from thirteen stations are utilized. Levelized economic cost is determined for economic feasibility analysis. … (more)
- Is Part Of:
- Renewable energy. Volume 168(2021)
- Journal:
- Renewable energy
- Issue:
- Volume 168(2021)
- Issue Display:
- Volume 168, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 168
- Issue:
- 2021
- Issue Sort Value:
- 2021-0168-2021-0000
- Page Start:
- 1229
- Page End:
- 1248
- Publication Date:
- 2021-05
- Subjects:
- Weibull distribution -- Energy assessment -- Optimization algorithm -- Levelized economic cost -- Kolmogorov–smirnov statistic
Renewable energy sources -- Periodicals
Power resources -- Periodicals
Énergies renouvelables -- Périodiques
Ressources énergétiques -- Périodiques
333.794 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09601481 ↗
http://www.elsevier.com/journals ↗
http://www.journals.elsevier.com/renewable-energy/ ↗ - DOI:
- 10.1016/j.renene.2021.01.008 ↗
- Languages:
- English
- ISSNs:
- 0960-1481
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7364.187000
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British Library HMNTS - ELD Digital store - Ingest File:
- 15593.xml