Abstract
The efficiency of a wind turbine highly depends on the value of tip speed ratio during its operation. The power coefficient of a wind turbine varies with tip speed ratio. For maximum power extraction, it is very important to hold the tip speed ratio at optimum value and operate the variable-speed wind turbine at its maximum power coefficient. In this paper, an intelligent learning based adaptive neuro-fuzzy inference system (ANFIS) is proposed for online estimation of tip speed ratio (TSR) as a function of wind speed and rotor speed. The system is developed by assigning fuzzy membership functions (MFs) to the input-output variables and artificial neural network (ANN) is applied to train the system using back propagation gradient descent algorithm and least square method. During the training process, the ANN adjusts the shape of MFs by analyzing training data set and automatically generates the decision making fuzzy rules. The simulations are done in MATLAB for standard offshore 5 MW baseline wind turbine developed by national renewable energy laboratory (NREL). The performance of proposed neuro-fuzzy algorithm is compared with conventional multilayer perceptron feed-forward neural network (MLPFFNN). The results show the effectiveness of proposed model. The proposed system is more reliable for accurate estimation of tip speed ratio.
Highlights
AND MOTIVATIONWind is a sustainable source of energy and is considered as a best alternate of limited fossil fuel resources
The wind turbine efficiency depends on the value of power coefficient, which varies with the tip speed ratio (TSR)
A hybrid intelligent methodology based of adaptive neuro-fuzzy inference system (ANFIS) is proposed for accurate estimation of TSR
Summary
Wind is a sustainable source of energy and is considered as a best alternate of limited fossil fuel resources. The optimal TSR for a 3 blade wind turbine is 5 which can be further improved up to 7 or 8 by using highly efficient and well-designed airfoil rotor blades. It is taken as 7 for a 3 blade wind turbine. An adaptive perturbation and observation (P&O) method was proposed in [10] for estimation of TSR and maximum power point tracking (MPPT). The major aim of this study is to propose a hybrid intelligent learning based neuro-fuzzy methodology for accurate estimation of TSR for offshore 5 MW baseline wind turbine.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.