Abstract
The accuracy in analysis of wind speed is very critical to assess wind potential at any site. Wind power potential has been estimated using statistical distribution methods at numerous places around the world. The main aim of this article is to analyse wind potential and to compare between metaheuristic optimization algorithms and numerical approaches utilising the wind data at various places in India measured from masts and remote sensing technologies. The Weibull distribution fitness test is calculated using real-time wind data from various locations. The optimal Weibull parameters are estimated using numerical methods such as empirical method of Justus, maximum likelihood method, graphical method, modified maximum likelihood method and Wind Atlas Analysis and Application Program (WAsP). Furthermore, to assess Weibull distribution function for different sites (onshore, nearshore and offshore) in India, the social spider optimization is compared to particle swarm optimization and genetic algorithm. To examine the accuracy of various approaches, further goodness-of-fit method is estimated. The mean power density is maximum for offshore, followed by nearshore and onshore site with 452.32 W/m2, 431.53 W/m2, and 283 W/m2, respectively, at 120 m height. WAsP approach outperforms other numerical approaches used in this work. When compared to the genetic algorithm, the social spider optimization and particle swarm optimization were shown to be more efficient. The suggested method is more accurate than the numerical approaches utilised for wind potential assessment, according to the results.
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The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- EMJ:
-
Empirical method of Justas
- MLM:
-
Maximum likelihood method
- GM:
-
Graphical method
- MMLM:
-
Modified maximum likelihood method
- EPFM:
-
Energy pattern factor method
- WAsP:
-
Wind atlas analysis and application program
- GAO:
-
Genetic algorithm optimization
- EEM:
-
Energy equivalent method
- GWOA:
-
Grey wolf optimizer algorithm
- MOM:
-
Method of moment
- ABCOA:
-
Artificial bee colony optimization algorithm
- r1, r2:
-
Random numbers
- Xl ,best, i :
-
Best population of l variable and i iteration
- MVO:
-
Multiverse optimization
- X′l , n , i :
-
Population update for l variable, n population, and i iteration
- BOA:
-
Bat optimization algorithm
- MFO:
-
Moth flame optimizations
- LSE:
-
Least square estimation
- SSO:
-
Social spider optimization
- PSO:
-
Particle swarm optimization
- GA:
-
Genetic algorithm
- ESA:
-
Evolutionary statistical approach
- RMSE:
-
Root mean square error
- MBE:
-
Mean bas error
- NRMSE:
-
Normalized root mean square error
- LiDAR:
-
Light detection and ranging
- SODAR:
-
Sound detection and ranging
- Xl ,worst, i :
-
Worst population of l variable and i iteration
- c1, c2:
-
Acceleration coefficients
- Xl , n , i :
-
Existing population
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The researchers are thankful to the assistance offered by the faculties of NIT Bhopal for providing the support to facilitate this study.
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HP contributed to conceptualization, methodology, data curation, writing—original draft, review and editing, software. VS helped in visualization, software, validation, resources. PB contributed to review, supervision. AS contributed to review and supervision.
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Patidar, H., Shende, V., Baredar, P. et al. Comparative analysis of wind potential and characteristics using metaheuristic optimization algorithms at different places in India. Int. J. Environ. Sci. Technol. 20, 13819–13834 (2023). https://doi.org/10.1007/s13762-022-04678-8
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DOI: https://doi.org/10.1007/s13762-022-04678-8