Effectiveness of nature-inspired algorithms using ANFIS for blade design optimization and wind turbine efficiency
Blade design of the horizontal axis wind turbine (HAWT) is an important parameter that determines the reliability and efficiency of a wind turbine. It is important to optimize the capture of the energy in the wind that can be correlated to the power coefficient (Cp) of HAWT system. In this paper,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English English |
| Published: |
MDPI
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/79847/1/79847_Effectiveness%20of%20Nature-Inspired%20Algorithms_article.pdf http://irep.iium.edu.my/79847/2/79847_Effectiveness%20of%20Nature-Inspired%20Algorithms_scopus_new.pdf http://irep.iium.edu.my/79847/3/79847_Effectiveness%20of%20Nature-Inspired%20Algorithms_wos.pdf http://irep.iium.edu.my/79847/ https://www.mdpi.com/2073-8994/11/4/456/htm |
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| Summary: | Blade design of the horizontal axis wind turbine (HAWT) is an important parameter that
determines the reliability and efficiency of a wind turbine. It is important to optimize the capture
of the energy in the wind that can be correlated to the power coefficient (Cp) of HAWT system.
In this paper, nature-inspired algorithms, e.g., ant colony optimization (ACO), artificial bee colony
(ABC), and particle swarm optimization (PSO) are used to search for the blade parameters that can
give the maximum value of Cp for HAWT. The parameters are tip speed ratio, blade radius, lift to
drag ratio, solidity ratio, and chord length. The performance of these three algorithms in obtaining
the optimal blade design based on the Cp are investigated and compared. In addition, an adaptive
neuro-fuzzy interface (ANFIS) approach is implemented to predict the Cp of wind turbine blades for
investigation of algorithm performance based on the coefficient determination (R2) and root mean
square error (RMSE). The optimized blade design parameters are validated with experimental results
from the National Renewable Energy Laboratory (NREL). It was found that the optimized blade
design parameters were obtained using an ABC algorithm with the maximum value power coefficient
higher than ACO and PSO. The predicted Cp using ANFIS-ABC also outperformed the ANFIS-ACO
and ANFIS-PSO. The difference between optimized and predicted is very small which implies the
effectiveness of nature-inspired algorithms in this application. In addition, the value of RMSE and R2
of the ABC-ANFIS algorithm were lower (indicating that the result obtained is more accurate) than
the ACO and PSO algorithms |
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