Nonsingular Terminal Sliding Mode Control With Ultra-Local Model and Single Input Interval Type-2 Fuzzy Logic Control for Pitch Control of Wind Turbines

As wind energy is becoming one of the fastestgrowing renewable energy resources,controlling large-scale wind turbines remains a challenging task due to its system model nonlinearities and high external uncertainties.The main goal of the current work is to propose an intelligent control of the wind turbine system without the need for model identification.For this purpose,a novel model-independent nonsingular terminal slidingmode control(MINTSMC)using the basic principles of the ultralocal model(ULM)and combined with the single input interval type-2 fuzzy logic control(SIT2-FLC)is developed for non-linear wind turbine pitch angle control.In the suggested control framework,the MINTSMC scheme is designed to regulate the wind turbine speed rotor,and a sliding-mode(SM)observer is adopted to estimate the unknown phenomena of the ULM.The auxiliary SIT2-FLC is added in the model-independent control structure to improve the rotor speed regulation and compensate for the SM observation estimation error.Extensive examinations and comparative analyses were made using a real-time softwarein-the-loop(RT-SiL)based on the dSPACE 1202 board to appraise the efficiency and applicability of the suggested modelindependent scheme in a real-time testbed.     

Design of low noise airfoil with high aerodynamic performance for use on small wind turbines

Wind power is one of the most reliable renewable energy sources and internationally installed capacity is increasing radically every year.Although wind power has been favored by the public in general,the problem with the impact of wind turbine noise on people living in the vicinity of the turbines has been increased.Low noise wind turbine design is becoming more and more important as noise is spreading more adverse effect of wind turbine to public.This paper demonstrates the design of 10 kW class wind turbi...     

Adaptive and Predictive Control Strategies for Wind Turbine Systems: A Survey

The wind turbine (WT) is a renewable energy conversion device for transformation of kinetic energy from the wind to mechanical energy for subsequent use in different forms. This paper focuses on wind turbine control design strategies. The content is divided into the following parts: 1) An overview of the recent advances that have been made in the application of adaptive and model predictive control strategies for wind turbines. 2) Summarizes some important aspects of modeling of wind turbines for control studies. 3) Provides an outlook on the application of adaptive model predictive control for uncertain systems to stimulate new research interests for wind turbine systems. We provide an overall picture of the research results with evaluation of the merits/demerits.      

A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization

Wind energy has been widely applied in power generation to alleviate climate problems. The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream. Wind farm layout optimization (WFLO) aims to reduce the wake effect for maximizing the power outputs of the wind farm. Nevertheless, the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm, which severely affect power conversion efficiency. Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios. Thus, a chaotic local search-based genetic learning particle swarm optimizer (CGPSO) is proposed to optimize large-scale WFLO problems. CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms. The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance, stability, and robustness. To be specific, a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local. It improves the solution quality. The parameter and search pattern of chaotic local search are also analyzed for WFLO problems.      

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.     

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。     

Adaptive Continuous Neural Pitch Angle Control for Variable‐Speed Wind Turbines

As wind energy becomes one of the fastest growing renewable energy resources, the control of large‐scale wind turbines remains a challenging task due to its system model nonlinearities and high external uncertainties. In this paper, an adaptive neural pitch angle control strategy is proposed for the variable‐speed wind turbines (VSWT) operating in pitch control region. The control objective is to maintain the rotor speed and generator power at the prescribed reference values in the presence of external disturbance, without the need of the information of system parameters and aerodynamics. First, the order of the system dynamics is increased by defining a filtered regulation error. By this means, the non‐affine characteristics of the VSWT model is transformed into a simple affine control problem and thus the feedback linearization technique can be employed. The continuousness of control signal is also guaranteed to relax the requirement on the bandwidth of actuators, and the mechanical load on pitching systems is reduced. Subsequently, an online learning approximator (OLA) is utilized to estimate the unknown nonlinear aerodynamics of the wind turbine and extend the practicability of the proposed adaptive parameter‐free controller. In addition, a high‐gain observer is implemented to obtain an estimation of rotor acceleration, which rejects the need of additional sensors. Rigid theoretical analysis guarantees the tracking of rotor speed/generator power and the boundedness of all other signals of the closed‐loop system. Finally, the effectiveness of the proposed scheme is testified via the Wind Turbine Blockset simulation package in Matlab/Simulink environment. Moreover, comparison results reveal that the introduced solution is able to provide better regulation performance than the conventional PI counterpart.     

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。     

风电场中嵌入式通信服务模块设计

针对风电场原有组件因受自身硬件与软件资源的局限而不能与下一代风电机组实现无缝通信的问题,提出在风电场各组件中遵循风电场监控通信标准IEC61400-25的嵌入式通信服务模块的解决方案,设计ARM+Linux+J2ME平台并采用MMS技术与Web服务的方法提供统一的通信方式与通信接口,结果证明该方案能有效解决工程中监控系统难以兼容、风电场统一操作和维护困难的问题。     

Overview of modelling and control strategies for wind turbines and wave energy devices: Comparisons and contrasts

Increasingly, there is a focus on utilising renewable energy resources in a bid to fulfil increasing energy requirements and mitigate the climate change impacts of fossil fuels. While most renewable resources are free, the technology used to usefully convert such resources is not and there is an increasing focus on improving the conversion economy and efficiency. To this end, advanced control technology can have a significant impact and is already a relatively mature technology for wind turbines. Though wave energy systems are still in their infancy, significant benefits have been shown to accrue from the appropriate use of control technology. To date, the application communities connected with wind and wave energy have had little communication, resulting in little cross fertilisation of control ideas and experience, particularly from the more mature wind area to wave. This paper examines the application of control technology across both domains, both from a comparative and contrasting point of view, with the aim of identifying commonalities in control objectives and potential solutions. Key comparative reference points include the articulation of the stochastic resource models, specification of control objectives, development of realistic device models, and development of solution concepts. Not least, in terms of realistic system requirements are the set of physical and legislative constraints under which such renewable energy systems must operate, and the need to provide reliable and fault-tolerant control solutions, which respect the often remote and relatively inaccessible location of many offshore deployments.     

Optimizing wind farm cable layout considering ditch sharing

Wind power is becoming an important source of electrical energy production. In an onshore wind farm (WF), the electrical energy is collected at a substation from different wind turbines through electrical cables deployed over ground ditches. This work considers the WF layout design assuming that the substation location and all wind turbine locations are given, and a set of electrical cable types is available. The WF layout problem, taking into account its lifetime and technical constraints, involves selecting the cables to interconnect all wind turbines to the substation and the supporting ditches to minimize the initial investment cost plus the cost of the electrical energy that is lost on the cables over the lifetime of the WF. It is assumed that each ditch can deploy multiple cables, turning this problem into a more complex variant of previously addressed WF layout problems. This variant turns the problem best fitting to the real case and leads to substantial gains in the total cost of the solutions. The problem is defined as an integer linear programming model, which is then strengthened with different sets of valid inequalities. The models are tested with four WFs with up to 115 wind turbines. The computational experiments show that the optimal solutions can be computed with the proposed models for almost all cases. The largest WF was not solved to optimality, but the final relative gaps are small.     

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.     

Control co-design of 13 MW downwind two-bladed rotors to achieve 25% reduction in levelized cost of wind energy

Wind energy is recognized worldwide as cost-effective and environmentally friendly and is among the fastest-growing sources of electrical energy. To further decrease the cost of wind energy, wind turbines are being designed at ever larger scales, which is challenging due to greater structural loads and deflections. Large-scale systems such as modern wind turbines increasingly require a control co-design approach, whereby the system design and control design are performed in a more integrated fashion. We overview a two-bladed downwind morphing rotor concept that is expected to lower the cost of energy at wind turbine sizes beyond 13 megawatts (MW) compared with continued upscaling of traditional three-bladed upwind rotor designs. We describe an aero-structural-control co-design process that we have used in designing such extreme-scale wind turbines, and we discuss how we were able to achieve a 25% reduction in levelized cost of energy for our final turbine design compared to a conventional upwind three-bladed rotor design.     

Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networks

In wind energy conversion systems, one of the operational problems is the changeability and discontinuity of wind. In most cases, wind speed can fluctuate rapidly. Hence, quality of produced energy becomes an important problem in wind energy conversion plants. Several control techniques have been applied to improve the quality of power generated from wind turbines. Pitch control is the most efficient and popular power control method, especially for variable-speed wind turbines. It is a useful method for power regulation above the rated wind speed. This paper proposes an artificial neural network-based pitch angle controller for wind turbines. In the simulations, a variable-speed wind turbine is modeled, and its operation is observed by using two types of artificial neural network controllers. These are multi-layer perceptrons with back propagation learning algorithm and radial basis function network. It is shown that the power output was successfully regulated during high wind speed, and as a result overloading or outage of the wind turbine was prevented.     

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.     

Variable Parameter Nonlinear Control for Maximum Power Point Tracking Considering Mitigation of Drive-train Load

Since mechanical loads exert a significant influence on the life span of wind turbines, the reduction of transient load on drive-train shaft has received more attention when implementing a maximum power point tracking (MPPT) controller. Moreover, a trade-off between the efficiency of wind energy extraction and the load level of drive-train shaft becomes a key issue. However, for the existing control strategies based on nonlinear model of wind turbines, the MPPT efficiencies are improved at the cost of the intensive fluctuation of generator torque and significant increase of transient load on drive train shaft. Hence, in this paper, a nonlinear controller with variable parameter is proposed for improving MPPT efficiency and mitigating transient load on drive-train simultaneously. Then, simulations on FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code and experiments on the wind turbine simulator (WTS) based test bench are presented to verify the efficiency improvement of the proposed control strategy with less cost of drive-train load.      

风电机组电动变桨距控制系统的优化研究

由于风速的随机性、不稳定性及气动效应的影响,使得风力发电机组变桨距控制系统具有非线性、参数时变性、强耦合等特点,难于实现高精度控制,导致风电机组输出电能质量较差。为了改善系统在恒功率输出运行区域内的动态性能,分析了风电机组变桨距控制系统的现状,建立了整个风电机组模型,提出了优化的变桨距控制策略,并设计了基于模糊控制的变桨距控制器。仿真结果表明,独立变桨距控制技术的控制效果比统一变桨距好,实现了风力机各叶片的优化独立变桨距控制,优化了风力发电系统在超过额定风速时的恒功率控制,具有抗干扰能力强、控制精度高的特点。     

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.     

Synthesis of nonlinear controller for wind turbines stability when providing grid support

This paper presents a new nonlinear polynomial controller for wind turbines that assures stability and maximizes the energy produced while imposing a bound in the generated power derivative in normal operation (guarantees a smooth operation against wind turbulence). The proposed controller structure also allows eventually producing a transient power increase to provide grid support, in response to a demand from a frequency controller. The controller design uses new optimization over polynomials techniques, leading to a tractable semidefinite programming problem. The ability of the wind turbine to increase its power under partial load operation has been analysed. The aforementioned optimization techniques have allowed quantifying the maximum transient overproduction that can be demanded to the wind turbine without violating minimum speed constraints (that could lead to unstable behaviour), as well as the total generated energy loss. The ability to evaluate this shortfall has permitted the development of an optimization procedure in which wind farm overproduction requirements are divided into individual turbines, assuring that the total energy loss in the wind farm is minimum, while complying with the maximum demanded power constraints. Copyright © 2013 John Wiley & Sons, Ltd.     

Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms

Wind energy has become the world’s fastest growing energy source. Although wind farm layout is a well known problem, its solution used to be heuristic, mainly based on the designer experience. A key in search trend is to increase power production capacity over time. Furthermore the production of wind energy often involves uncertainties due to the stochastic nature of wind speeds. The addressed problem contains a novel aspect with respect of other wind turbine selection problems in the context of wind farm design. The problem requires selecting two different wind turbine models (from a list of 26 items available) to minimize the standard deviation of the energy produced throughout the day while maximizing the total energy produced by the wind farm. The novelty of this new approach is based on the fact that wind farms are usually built using a single model of wind turbine. This paper describes the usage of multi-objective evolutionary algorithms (MOEAs) in the context of power energy production, selecting a combination of two different models of wind turbine along with wind speeds distributed over different time spans of the day. Several MOEAs variants belonging to the most renowned and widely used algorithms such as SPEA2 NSGAII, PESA and msPEA have been investigated, tested and compared based on the data gathered from Cancun (Mexico) throughout the year of 2008. We have demonstrated the powerful of MOEAs applied to wind turbine selection problem (WTS) and estimate the mean power and the associated standard deviation considering the wind speed and the dynamics of the power curve of the turbines. Among them, the performance of PESA algorithm looks a little bit superior than the other three algorithms. In conclusion, the use of MOEAs is technically feasible and opens new perspectives for assisting utility companies in developing wind farms.