An optimal fuzzy PI controller to capture the maximum power for variable-speed wind turbines

Wind energy conversion systems can work by fixed and variable speed using the power electronic converters. The variable-speed type is more desirable because of its ability to achieve maximum efficiency at all wind speeds. The main operational region for wind turbines according to wind speed is divided into partial load and full load. In the partial-load region, the main goal is to maximize the power captured from the wind. This goal can be achieved by controlling the generator torque such that the optimal tip speed ratio is tracked. Since the wind turbine systems are nonlinear in nature and due to modeling uncertainties, this goal is difficult to be achieved in practice. The proportional-integral (PI) controller, due to its robustness and simplicity, is very often used in practical applications, but finding its optimal gains is a challenging task. In this paper, to cope with nonlinearities and at the same time modeling uncertainties of wind turbines, a PI torque controller is proposed such that its optimal gains are derived via a novel scheme based on particle swarm optimization algorithm and fuzzy logic theory. The proposed method is applied to a 5-MW wind turbine model. The simulation results show the effectiveness of the proposed method in capturing maximum power in the partial-load region while coping well with nonlinearities and uncertainties.     

Control of variable speed wind turbines: Dynamic models

Owing to concern over the environment, there is much interest in renewable sources of electrical power generation, of which one of the most promising is wind power. Wind turbines exploit this energy source to directly generate electrical power. There are essentially two types of windturbines, namely constant speed and variable speed machines. The purpose of this paper is to investigate the dynamics of variable speed wind turbines and determine suitable models to support the control design task. A basic but widely used dynamic representation of variable speed wind turbines and the corresponding models of the control plant dynamics are initially discussed. More detailed, yet still simple models, are derived separately for the rotor aerodynamics, the drive-train dynamics and the power generation unit dynamics before being combined to form the complete model of the wind turbine dynamics. The resulting combined model, in addition to supporting the control design task, enables the extent, to which the basic models adequate represent the wind turbine, to be assessed. It is concluded that the basic models of variable speed wind turbines are not adequate and do not exhibit all the relevant aspects of the dynamics necessary to support the control design.     

基于FIR神经网络的风电场虚拟风速传感器

风电机组风速传感器易发故障,故障可能导致机组安全风险和发电量损失。针对现行的故障处理方法因与机组控制策略紧密耦合而日益面临挑战,提出了一种基于数据处理的虚拟风速传感器原理与方法：由风电场上风向测量风速计算下风向推算风速,用推算风速取代故障传感器。着重讨论了基于FIR神经网络的推算风速计算方法和计算模型,探讨了系统实现的关键技术。实验证明了虚拟传感器的误差在机组控制系统可接受的程度内。提出的方法独立于机组自身属性,具有普遍适用性,可部署在任意类型的场,在物理传感器故障时向机组提供风速信号,支撑风电机组持续安全运行。     

低风速风力机最大风能追踪的互补滑模控制

针对风力机系统在最大功率点跟踪(MPPT)阶段易受风速等不确定因素的影响,为了进一步提高风力机的风能捕获效率,本文在滑模控制的基础上提出了一种互补滑模控制方法.首先,建立了含有干扰项的风力机系统的线性化模型,采用广义滑模面与互补滑模面相结合的方法设计了互补滑模控制器,并在理论上证明了此控制方法能够有效保证风力机转速跟踪误差的收敛性,且能提高转速跟踪精度.其次,采用风力机专业仿真软件FAST对美国可再生能源实验室(NREL)的600 kW风力机进行了仿真实验,结果表明本文所提出的控制方法不但能提高风力机的风能捕获效率,而且能有效减小转速跟踪误差.最后,将本文所提方法与现有常见的几种控制方法相比较发现:风力机系统在互补滑模控制策略下,具有更高的风能捕获效率和更小的转速跟踪误差.     

基于参考输入优化的变速风电机组最大化风能捕获方法

变速风电机组在额定风速以下应用最大功率点跟踪实现最大化风能捕获. 然而, 大惯量风电机组在面对快 速波动的湍流风速时, 因转速调节慢而难以保持运行于最大功率点. 本文研究进一步发现, 平均转速跟踪误差与整 体的风能捕获效率并非单调关系, 这使得当前以减小转速跟踪误差为目标的控制器设计难以有效提升风电机组的 发电效率. 为此, 本文以提升风能捕获效率(而非减小转速跟踪误差)为目标, 提出一种基于参考输入优化的风电机 组最大化风能捕获方法. 考虑到参考转速对风能捕获效率的复杂影响难以准确建模, 本文借助深度确定性策略梯度 (DDPG)强化学习算法实现参考输入优化. 仿真结果表明该方法能够有效提升湍流风下变速风电机组的风能捕获效 率.     

Adaptive backstepping control of variable speed wind turbines

Variable speed wind turbines maximize the energy capture by operating the turbine at the peak of the power coefficient, however parametric uncertainties in mechanical and electrical dynamics of the system may limit the efficiency of the turbine. In this study, we present an adaptive backstepping approach for the variable speed control of wind turbines. Specifically, to overcome the undesirable effects of parametric uncertainties, a desired compensation adaptation law (DCAL) based controller has been proposed. The proposed method achieves global asymptotic rotor speed tracking, despite the parametric uncertainty on both mechanical and electrical subsystems. Extensive simulation studies are presented to illustrate the feasibility and efficiency of the method proposed.     

A control strategy for variable-speed variable-pitch wind turbines within the regions of partial- and full-load operation without wind speed feedback

In this paper, the control of a variable-speed variable-pitch wind turbine in the whole wind speed range is addressed, without any feedback measurement of wind speed. In addition to an aerodynamic torque observer able to ensure the tracking of the maximum delivered power in the partial-load region, a novel wind speed observer is proposed for power regulation in the full-load region, along with a sliding surface ensuring finite-time set-point stabilization of the speed tracking error. The proposed control solution has been validated on the National Renewable Energy Laboratory 5-MW three-blade wind turbine model using the recognized high-fidelity simulation tool FAST.     

A new sliding mode controller for wind turbine pitch angle control

This paper focuses on variable-speed wind turbines where control is aimed at stabilizing the output power. A hybrid control algorithm is proposed which includes a new arrangement of two controllers along with an observer. Estimation of power coefficient via sliding mode observer reduces the amount of errors caused by changing the power coefficient parameter in different turbines. Moreover, the use of this special arrangement of sliding mode controller, proportional-integral (PI) controller and sliding mode observer, removes one of the controller parameter denoting wind speed from the designed algorithm, which in turn eliminates disturbances related to wind speed changes. In order to show the efficiency of the proposed control method, the performances of the controllers are evaluated by simulation in MATLAB software.     

Sensorless adaptive output feedback control of wind energy systems with PMS generators

This paper addresses the problem of controlling wind energy conversion (WEC) systems involving permanent magnet synchronous generator (PMSG) fed by IGBT-based buck-to-buck rectifier–inverter. The prime control objective is to maximize wind energy extraction which cannot be achieved without letting the wind turbine rotor operate in variable-speed mode. Interestingly, the present study features the achievement of the above energetic goal without resorting to sensors of wind velocity, PMSG speed and load torque. To this end, an adaptive output-feedback control strategy devoid of any mechanical sensor is developed (called sensorless), based on the nonlinear model of the whole controlled system and only using electrical variables measurements. This control strategy involves: (i) a sensorless online reference-speed optimizer designed using the turbine power characteristic to meet the maximum power point tracking (MPPT) requirement; (ii) a nonlinear speed regulator designed by using the backstepping technique; (iii) a sensorless interconnected adaptive state observer providing online estimates of the rotor position as well as speed and load/turbine torque. The proposed output-feedback control strategy is backed by a formal analysis showing that all control objectives are actually achieved. Several simulations show that the control strategy enjoys additional robustness properties.     

Optimization of variable speed wind power systems based on a LQG approach

This paper focuses on a control application of optimization in wind power systems. An optimal control structure for variable speed fixed pitch wind turbines is presented. The optimality of the whole system is defined by the trade-off between the energy conversion maximization and the control input minimization that determines the mechanical stress of the drive train. The frequency separation of the short-term and the long-term variations, adopted in the wind modelling, has resulted in a two-loop control structure. The optimal problem is treated within a complete linear quadratic stochastic approach, whose effectiveness was tested on an electromechanical wind turbine simulator.     

具有尾缘襟翼风力机的恒功率反步法控制

尾缘襟翼风力机控制技术在大型风力机领域具有巨大的应用潜力.本文首先基于修正的叶素动量方法建立了具有可变尾缘襟翼的风力机气动模型.针对襟翼风力机的非线性模型,采用反步法设计了非线性控制器,保证系统的控制量和状态变量全局有界,并且风机的输出功率可以收敛到额定功率的一个小邻域内.此外,控制器设计过程中没有将实时风速信息纳入反馈系统,因而降低了工程实施的难度.最后针对12 m/s～15 m/s的阶跃风、基于四分量模型模拟的风载扰动、执行机构受到外部扰动以及总转动惯量具有10%不确定性的工况进行了仿真,仿真结果表明所设计的控制器能有效地稳定风力发电系统的输出功率,控制系统具有较强的鲁棒性.     

Modeling and simulation of wind turbine Savonius rotors using artificial neural networks for estimation of the power ratio and torque

The power factor and torque of wind turbines are predicted using artificial neural networks (ANNs) based on experimental data which have been collected for seven prototype vertical Savonius rotors tested in a wind tunnel. In this research, the rotors with different configurations were located in the wind tunnel and the tests were repeated 4–6 times in order to reduce errors. Since the Reynolds number has a negligible effect on power ratio, therefore tip speed ratio (TSR) is the main input parameter to be predicted in neural network. Also, the rotor’s power factor and torque were simulated for different tip speed ratios and different blade angles. The simulated results show a strong capability for providing reasonable predictions and estimations of the maximum power of rotors and maximizing the efficiency of Savonius turbines. According to artificial neural nets simulations and the experimental results, increasing tip speed ratio leads to a higher power ratio and torque. For all the tested rotors, a maximum and minimum amount of torque has happened at angle of 60^o and 120^o, respectively.     

Intelligent control for large-scale variable speed variable pitch wind turbines

Large-scale wind turbine generator systems have strong nonlinear multivariable characteristics with many uncertain factors and disturbances. Automatic control is crucial for the efficiency and reliability of wind turbines. On the basis of simplified and proper model of variable speed variable pitch wind turbines, the effective wind speed is estimated using extended Kalman filter. Intelligent control schemes proposed in the paper mchde two loops which operate in synchronism with each other. At below-rated wind speed, the inner loop adopts adaptive fuzzy control based on variable universe for generator torque regulation to realize maximum wind energy capture. At above-rated wind speed, a controller based on least square support vector machine is proposed to adjust pitch angle and keep rated output power. The simulation shows the effectiveness of the intelligent control.     

An observer-based blade-pitch controller of wind turbines in high wind speeds

The paper focuses on variable-rotor-speed/variable-blade-pitch wind turbines operating in the region of high wind speeds, where blade pitch and generator torque controllers are aimed at limiting the turbine's energy capture to the rated power value. Coupled design is described of an observer-based blade-pitch control input and a generator torque controller, both of which not requiring the availability of wind speed measurements. Closed loop convergence of the overall control system is proved. The proposed control solution has been validated on a 5-MW three-blade wind turbine using the National Renewable Energy Laboratory (NREL) wind turbine simulator FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code.     

Comparison between linear and nonlinear control strategies for variable speed wind turbines

The purpose of this work is to compare some linear and nonlinear control strategies, with the aim of benefiting as well as possible of wind energy conversion systems. Below rated wind speed, the main control objective is to perform an optimal wind power capture while avoiding strong loads on the drive train shafts. To explicitly take into consideration the low speed shaft flexibility, a two-mass nonlinear model of the wind turbine is used for controllers synthesis. After adapting a LQG controller based on the linearized model, nonlinear controllers based on a wind speed estimator are developed. They take into account the nonlinear dynamic aspect of the wind turbine and the turbulent nature of the wind. The controllers are validated upon an aeroelastic wind turbine simulator for a realistic wind speed profile. The study shows that nonlinear control strategies bring more performance in the exploitation of wind energy conversion systems.     

小型H 型垂直轴风车叶片的设计分析

风力发电机（简称风车）,是一种将风能转化为机械能,电能或热能的转 换装置。比较了垂直轴风力发电机与水平轴风力发电机的优势后,对小型H 型垂直轴风车 叶片的进行了分析,给出了在不同工作环境中的载荷分析,并对叶片的应力计算及校核方法 作了讨论,为小型H 型垂直轴风车的叶片设计提供了参考。     

Control of variable speed wind turbines: Design task

Owing to concern over the environment, there is much interest in renewable sources of electrical power generation, of which one of the most promising is wind power. There are essentially two types of wind turbines, namely constant speed and variable speed machines. In comparison to constant speed wind turbines, variable speed wind turbines are perceived to have several potential advantages which outweigh the considerable cost of the power electronics required to realize variable speed operation. The two frequently mentioned ones are: additional energy capture below rated wind speed and additional power-train compliance and associated load alleviation above rated wind speed. The purpose of this paper is to investigate the design of the control system (as opposed to the synthesis of the controller) for variable speed wind turbines. The choice of control strategy is investigated and appropriate realizations of the controller, to cater for the implementation issues of accommodation of variation in the plant dynamics over the operational envelope and switching transients, are identified. A thorough investigation of the dynamics and control of both stall regulated and pitch regulated variable speed machines is undertaken.     

大型风力发电机组控制器优化设计

风力发电机组是复杂多变量非线性系统,具有不确定性和多干扰等特点.目前国内应用的风力发电机组大多采用变桨变速恒频系统,具有风能利用系数高、安全可靠、改善整机受力等优点,然而依然面临载荷过大、功率波动大和变桨过于频繁等问题,因此,在大型风力发电机组系统中,选择最适合控制策略的控制器的优化设计就显得尤为重要.在此提出基于模糊性能评估器的控制方法,建立非线性控制模型并设计模糊控制器,介绍了控制器的优化设计步骤,仿真结果表明了这种方法的有效性.     

Prediction of aerodynamics performance of continuously variable-speed wind turbine by adaptive neuro-fuzzy methodology

Horizontal-axis wind turbines (HAWT) have the constant rotor speed, while the blade tip speed changes continuously. This could reduce power performance of the wind turbine. In this paper, the accuracy of soft-computing technique was employed for aerodynamics performance prediction based on continuously variable-speed horizontal-axis wind turbine with optimal blades. The process, which simulates the $$\varphi$$ (relative wind angle), BEP (blade element parameter), SP (solidity parameter), CPtot (total power coefficient), CPl (local power coefficient), and CT (local thrust coefficient), with adaptive neuro-fuzzy inference system (ANFIS) was constructed. The inputs were local speed ratios λr and different values of drag-to-lift ratio ε. The performance of proposed system is confirmed by the simulation results. The ANFIS results are compared with the experimental results using root-mean-square error and coefficient of determination and Pearson’s coefficient. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The effectiveness of the proposed strategies is verified based on the simulation results.     

Short-term wind speed forecasting based on a hybrid model

Wind power is currently one of the types of renewable energy with a large generation capacity. However, operation of wind power generation is very challenging because of the intermittent and stochastic nature of the wind speed. Wind speed forecasting is a very important part of wind parks management and the integration of wind power into electricity grids. As an artificial intelligence algorithm, radial basis function neural network (RBFNN) has been successfully applied into solving forecasting problems. In this paper, a novel approach named WTT–SAM–RBFNN for short-term wind speed forecasting is proposed by applying wavelet transform technique (WTT) into hybrid model which hybrids the seasonal adjustment method (SAM) and the RBFNN. Real data sets of wind speed in Northwest China are used to evaluate the forecasting accuracy of the proposed approach. To avoid the randomness caused by the RBFNN model or the RBFNN part of the hybrid model, all simulations in this study are repeated 30 times to get the average. Numerical results show that the WTT–SAM–RBFNN outperforms the persistence method (PM), multilayer perceptron neural network (MLP), RBFNN, hybrid SAM and RBFNN (SAM–RBFNN), and hybrid WTT and RBFNN (WTT–RBFNN). It is concluded that the proposed approach is an effective way to improve the prediction accuracy.