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 <inline-formula> <math displa...
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MDPI AG
2019-04-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/11/4/456 |
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| author | Md. Rasel Sarkar Sabariah Julai Chong Wen Tong Siti Fauziah Toha |
| author_facet | Md. Rasel Sarkar Sabariah Julai Chong Wen Tong Siti Fauziah Toha |
| author_sort | Md. Rasel Sarkar |
| collection | DOAJ |
| description | 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <mrow> <mi>C</mi> <mi>p</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> </inline-formula> 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mi>C</mi> <mi>p</mi> </mrow> </semantics> </math> </inline-formula> 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mi>C</mi> <mi>p</mi> </mrow> </semantics> </math> </inline-formula> are investigated and compared. In addition, an adaptive neuro-fuzzy interface (ANFIS) approach is implemented to predict the <inline-formula> <math display="inline"> <semantics> <mrow> <mi>C</mi> <mi>p</mi> </mrow> </semantics> </math> </inline-formula> of wind turbine blades for investigation of algorithm performance based on the coefficient determination (<i>R</i><sup>2</sup>) 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mi>C</mi> <mi>p</mi> </mrow> </semantics> </math> </inline-formula> 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 <i>R</i><sup>2</sup> of the ABC-ANFIS algorithm were lower (indicating that the result obtained is more accurate) than the ACO and PSO algorithms. |
| first_indexed | 2024-04-11T11:55:08Z |
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| last_indexed | 2024-04-11T11:55:08Z |
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| spelling | doaj.art-663a77e6d89248649cb90d0f1cd27ab82022-12-22T04:25:11ZengMDPI AGSymmetry2073-89942019-04-0111445610.3390/sym11040456sym11040456Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine EfficiencyMd. Rasel Sarkar0Sabariah Julai1Chong Wen Tong2Siti Fauziah Toha3Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, MalaysiaDepartment of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, MalaysiaDepartment of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, MalaysiaDepartment of Mechatronics Engineering, Kulliyyah of Engineering, International Islamic University Malaysia, 53100 Gombak, Selangor, MalaysiaBlade 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <mrow> <mi>C</mi> <mi>p</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> </inline-formula> 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mi>C</mi> <mi>p</mi> </mrow> </semantics> </math> </inline-formula> 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mi>C</mi> <mi>p</mi> </mrow> </semantics> </math> </inline-formula> are investigated and compared. In addition, an adaptive neuro-fuzzy interface (ANFIS) approach is implemented to predict the <inline-formula> <math display="inline"> <semantics> <mrow> <mi>C</mi> <mi>p</mi> </mrow> </semantics> </math> </inline-formula> of wind turbine blades for investigation of algorithm performance based on the coefficient determination (<i>R</i><sup>2</sup>) 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mi>C</mi> <mi>p</mi> </mrow> </semantics> </math> </inline-formula> 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 <i>R</i><sup>2</sup> of the ABC-ANFIS algorithm were lower (indicating that the result obtained is more accurate) than the ACO and PSO algorithms.https://www.mdpi.com/2073-8994/11/4/456optimizationblade design parameterscoefficient of performanceant colony optimizationparticle swarm optimizationartificial bee colonyANFIS |
| spellingShingle | Md. Rasel Sarkar Sabariah Julai Chong Wen Tong Siti Fauziah Toha Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency Symmetry optimization blade design parameters coefficient of performance ant colony optimization particle swarm optimization artificial bee colony ANFIS |
| title | Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency |
| title_full | Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency |
| title_fullStr | Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency |
| title_full_unstemmed | Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency |
| title_short | Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency |
| title_sort | effectiveness of nature inspired algorithms using anfis for blade design optimization and wind turbine efficiency |
| topic | optimization blade design parameters coefficient of performance ant colony optimization particle swarm optimization artificial bee colony ANFIS |
| url | https://www.mdpi.com/2073-8994/11/4/456 |
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