Abstract
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 ( C p ) 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 C p 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 C p are investigated and compared. In addition, an adaptive neuro-fuzzy interface (ANFIS) approach is implemented to predict the C p 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 C p 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.
Highlights
Renewable energy, such as wind energy, biomass energy, hydro energy, and solar energy have been extensively harnessed and exploited during the last decade because of environmental concerns.The most promising source of renewable energy is wind energy due to the factor of low cost in comparison with other sources of renewable energy, such as solar energy and biomass energy, etc. [1,2].Wind energy is harnessed via the use of a wind turbine system that converts mechanical energy into electrical energy by using a generator
The best input combination of artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization (PSO) algorithms from Table 1 are used in ANFIS for the prediction of the power coefficient
The proposed ANFIS model used for this investigation has a very good correlation with the power coefficient measured for training and testing data sets of three algorithms ABC, ACO, and PSO
Summary
Renewable energy, such as wind energy, biomass energy, hydro energy, and solar energy have been extensively harnessed and exploited during the last decade because of environmental concerns. For the power coefficient optimization of wind turbine blades, the influence of lift to drag ratio, blade radius, tip-speed ratio, solidity ratio, and the chord length of the blade has been widely investigated. Safaei et al [17] proposed the new two-step PSO algorithm for the placement of wind turbine generators for maximum allowable capacity and minimizing power loss in wind turbines. Sedaghat and Mirhosseini [24] implemented blade element momentum theory (BEM) for a power coefficient of 300 kW in HAWT technology in the province of Semman in Iran They obtained the maximum power coefficient of 0.51 when the tip-speed ratio was up to its optimum value of 10. There is no particular study which focuses on the optimization of the power coefficient wind turbine blades by using three algorithms. An endeavor is prepared for retrieving the correlation between Cp and the best combination of optimized blade parameters such as lift and drag ratio, blade radius, tip speed ratio, solidity ratio, and chord length of the blade of HAWTs
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