Investigation on the impact of design wind speed and control strategy on the performance of fixed-pitch variable-speed wind turbines

Wind turbine blade design optimization remains one of the fundamental research areas for modern wind turbine technology. The general design process for fixed-pitch variablespeed wind turbine blades assumes the rated wind speed as the design wind speed. However, for a fixed-pitch wind turbine with fixed rotor diameter and rated power at rated speed, we do not know the optimum design wind speed, which should be used for the calculation of the wind turbine blade parameters based on a particular aerofoil for a specific site with low annual mean wind speed. This paper investigates the impact of design wind speed and control strategy on the performance of fixed-pitch wind turbines through a set of design case studies. The design wind speeds are considered at the more prevalent wind speeds than the rated wind speed. Three different control strategies are addressed, i.e. maximum power point tracking, mixture of variable-speed and fixed-speed, and over-speeding. Annual energy production, blade manufacturing cost, aerodynamic load performance and cost of energy are analyzed and compared using the design case studies. The results reveal a clear picture in determining the optimum design wind speed and control strategy for both maximizing annual energy production and minimizing cost of energy

Genetic algorithm optimization of PID pitch angle controller for a 2 MW wind turbine

Speed regulation of wind turbine rotors are controlled by pitch angle controllers that aect the life expectancy of wind turbines, reliability and power quality. Optimization of wind turbine pitch angle controllers perform crucial eect on the wind turbine dynamics where the speed stability is achieved. In today's modern and commercial wind turbines, blade pitch angle controllers are generally implemented with PI and PID techniques. Determining the controller gain coecients are one of the most signicant problems in order to show a more stable rotor dynamics that eventually leads to better wind turbine performance in terms of both mechanical and electrical qualities. Hence, PID controller was designed and optimized with genetic algorithm technique for a 2 MW DFIG type wind turbine under Matlab-Simulink environment. Gain parameters were optimized for a given wind speed prole from third zone and optimized gain coecients were achieved within the optimization study. A ontroller with an optimum gain coeffcients shows the superior performance than the regular PID performance

Desain Turbin Angin Horisontal untuk Area Kecepatan Angin Rendah dengan Airfoil S826

This research aims to determine performance of turbine rotor performance with a single rotor blade model with a diameter of 0.6 m that has been developed by NORCOWE, while for turbine rotor blades used is the NREL S826 airfoil series. The wind turbines are operated at wind speed intervals of 1-5 m / s. This parameter will also present data in the form of the optimal point of wind turbine rotation and rotor rotation speed. The pitch angles used are 25 °, 30 °, and 35 °. The pitch angle that affects the value of the ideal rotational speed with the highest optimization for the horizontal airfoil turbine S826 is 30 ° with a wind speed of 5 m / s and a rotation of 570 RPM. This is because the greater the pitch angle of the installation, the easier it will be to experience speed trimming but is vulnerable to too large an angle of attack that causes a stall

Modelling And Simulation Of A Wind Turbine With Doubly Fed Induction Generator In Full Load Operation

The paper focuses on modelling and simulation of a 5 MW wind turbine with doubly fed induction generator (DFIG) in full load operation. The wind turbine model is described mathematically and presented in simulation blocks. Through a computer simulation, the wind turbine behavior in full load operation is investigated. A speed controller is used to adjust the pitch angle of a rotor blade in high wind speed to limit the wind energy captured by the turbine to the nominal power value. By adjusting the pitch angle to 18.26° at wind speed 20 m/s, the wind turbine is protected from mechanical damage due to torque and power limitation. The simulation results obtained can be used as references for future optimization for the variable speed wind turbine operation

Aerodynamic shape optimization of wind turbine blades using a Reynolds‐averaged Navier–Stokes model and an adjoint method

Computational fluid dynamics (CFD) is increasingly used to analyze wind turbines, and the next logical step is to develop CFD‐based optimization to enable further gains in performance and reduce model uncertainties. We present an aerodynamic shape optimization framework consisting of a Reynolds‐averaged Navier Stokes solver coupled to a numerical optimization algorithm, a geometry modeler, and a mesh perturbation algorithm. To efficiently handle the large number of design variables, we use a gradient‐based optimization technique together with an adjoint method for computing the gradients of the torque coefficient with respect to the design variables. To demonstrate the effectiveness of the proposed approach, we maximize the torque of the NREL VI wind turbine blade with respect to pitch, twist, and airfoil shape design variables while constraining the blade thickness. We present a series of optimization cases with increasing number of variables, both for a single wind speed and for multiple wind speeds. For the optimization at a single wind speed performed with respect to all the design variables (1 pitch, 11 twist, and 240 airfoil shape variables), the torque coefficient increased by 22.4% relative to the NREL VI design. For the multiple‐speed optimization, the torque increased by an average of 22.1%. Depending on the CFD mesh size and number of design variables, the optimization time ranges from 2 to 24h when using 256 cores, which means that wind turbine designers can use this process routinely. Copyright © 2016 John Wiley & Sons, Ltd.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136413/1/we2070_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136413/2/we2070.pd

Adaptive Pitch Controller of a Large-Scale Wind Turbine Using Multi-Objective Optimization

This paper deals with the control problems of a wind turbine working in its nominal zone. In this region, the wind turbine speed is controlled by means of the pitch angle, which keeps the nominal power constant against wind fluctuations. The non-uniform profile of the wind causes tower displacements that must be reduced to improve the wind turbine lifetime. In this work, an adaptive control structure operating on the pitch angle variable is proposed for a nonlinear model of a wind turbine provided by FAST software. The proposed control structure is composed of a gain scheduling proportional–integral (PI) controller, an adaptive feedforward compensation for the wind speed, and an adaptive gain compensation for the tower damping. The tuning of the controller parameters is formulated as a Pareto optimization problem that minimizes the tower fore-aft displacements and the deviation of the generator speed using multi-objective genetic algorithms. Three multi-criteria decision making (MCDM) methods are compared, and a satisfactory solution is selected. The optimal solutions for power generation and for tower fore-aft displacement reduction are also obtained. The performance of these three proposed solutions is evaluated for a set of wind pattern conditions and compared with that achieved by a classical baseline PI controller

Deep neural learning based distributed predictive control for offshore wind farm using high fidelity LES data

The paper explores the deep neural learning (DNL) based predictive control approach for offshore wind farm using high fidelity large eddy simulations (LES) data. The DNL architecture is defined by combining the Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN) for feature extraction and prediction of the offshore wind farm. This hybrid CNN-LSTM model is developed based on the dynamic models of the wind farm and wind turbines as well as higher-fidelity LES data. Then, distributed and decentralized model predictive control (MPC) methods are developed based on the hybrid model for maximizing the wind farm power generation and minimizing the usage of the control commands. Extensive simulations based on a two-turbine and a nine-turbine wind farm cases demonstrate the high prediction accuracy (97% or more) of the trained CNN-LSTM models. They also show that the distributed MPC can achieve up to 38% increase in power generation at farm scale than the decentralized MPC. The computational time of the distributed MPC is around 0.7s at each time step, which is sufficiently fast as a real-time control solution to wind farm operations