Supervisory controller for reduction of wind turbine loads in curtailed operation

There are situations in which wind turbines must curtail their power, i.e. produce less power than is available from the wind. In such cases the wind turbine power can be increased or decreased if required. This gives an opportunity to strike a balance between varying power production and reducing wind turbine structural loading. To that end, a supervisory controller is designed that issues power references to the wind turbine and can be easily installed on already operational wind turbines. The wind turbine with a supervisory controller produces the required mean power, while reducing wind turbine loads by adding power variations. The extensive, realistic simulations are done to evaluate the influence of the proposed controller on the fatigue loads, extreme loads and the overall wind turbine operation. The results indicate that a significant reduction of fatigue loads can be achieved, which can increase the operating life of the structure. Furthermore, the proposed supervisory controller can be utilized as the main building block of a wind farm controller, which meets the grid code requirements and can be easily installed on very large wind farms due to minimal requirements on the farm-wide communication.     

Distributed Model Predictive Load Frequency Control of Multi-area Power System with DFIGs

Reliable load frequency control (LFC) is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-area interconnected power system with wind turbines, this paper presents a distributed model predictive control (DMPC) based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The generation rate constraints (GRCs), load disturbance changes, and the wind speed constraints are considered. Furthermore, the DMPC algorithm may reduce the impact of the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and without the participation of the wind turbines is carried out. Analysis and simulation results show possible improvements on closed-loop performance, and computational burden with the physical constraints.      

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.     

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.      

风电机组无功功率控制和频率调节建模与仿真研究

风电机组并入电网时,难免会导致电网运行的一些故障,其中的两种可能故障情形就是无功功率控制和频率调节。本文根据《大型风电场并网设计技术规范》中关于风电机组无功功率控制和频率调节的规定,利用Matlab/simulink来模拟风电机组并网时可能遇到的故障电压,分别模拟了风电机组满足功率因数在一定范围内动态可调和频率发生变化时的情形。仿真结果表明本文所设计的仿真系统能达到系统要求。     

Wind Farm Layout Design Using Cuckoo Search Algorithms

Wind energy has emerged as a strong alternative to fossil fuels for power generation. To generate this energy, wind turbines are placed in a wind farm. The extraction of maximum energy from these wind farms requires optimal placement of wind turbines. Due to complex nature of micrositing of wind turbines, the wind farm layout design problem is considered a complex optimization problem. In the recent past, various techniques and algorithms have been developed for optimization of energy output from wind farms. The present study proposes an optimization approach based on the cuckoo search (CS) algorithm, which is relatively a recent technique. A variant of CS is also proposed that incorporates a heuristic-based seed solution for a better performance. The proposed CS algorithms are compared with genetic and particle swarm optimization (PSO) algorithms, which have been extensively applied to wind farm layout design. Empirical results indicate that the proposed CS algorithms outperformed the genetic and PSO algorithms for the given test scenarios in terms of yearly power output and efficiency.     

Wind Farm Layout Design Using Cuckoo Search Algorithms

ABSTRACT

Wind energy has emerged as a strong alternative to fossil fuels for power generation. To generate this energy, wind turbines are placed in a wind farm. The extraction of maximum energy from these wind farms requires optimal placement of wind turbines. Due to complex nature of micrositing of wind turbines, the wind farm layout design problem is considered a complex optimization problem. In the recent past, various techniques and algorithms have been developed for optimization of energy output from wind farms. The present study proposes an optimization approach based on the cuckoo search (CS) algorithm, which is relatively a recent technique. A variant of CS is also proposed that incorporates a heuristic-based seed solution for better performance. The proposed CS algorithms are compared with genetic and particle swarm optimization algorithms which have been extensively applied to wind farm layout design. Empirical results indicate that the proposed CS algorithms outperformed the genetic and particle swarm optimization algorithms for the given test scenarios in terms of yearly power output and efficiency.     

输入时滞的海上风电机群分布式控制

随着海上风电的大规模发展, 以及电网互联范围的不断扩大, 时滞主要产生在风电场广域测量系统的信号 测量和传输中, 从而导致风电机组系统性能下降甚至不稳定, 严重影响了风电场的正常运行. 因此, 该文基于Hamilton 能量理论, 研究了双馈风电机群的分布式时滞控制问题. 首先, 该文对双馈风电系统进行Hamilton实现, 得到风 电机组PCH–D模型; 然后, 针对单机系统PCH–D模型, 引入Casimir函数设计控制律, 使得风电系统在输入时滞下能 够稳定运行; 进而, 将风电机组网络化, 设计分布式时滞控制策略, 使得整个风电机群在30300 ms的时滞范围内整 体保持稳定. 最后, 通过仿真验证了所提控制策略能有效地解决系统的输入时滞问题, 同时减小了时滞引起的系统 误差, 提高了风电机群的稳定性和控制精度.     

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.     

Energy efficient control of a stand-alone wind energy conversion system with AC current harmonics compensation

This paper presents a new energy-efficient control strategy for a variable-speed wind energy conversion system (WECS). The considered WECS is designed for dc load supply and battery charging in stand-alone applications. The batteries are charged through a three-phase full-bridge power converter. The WECS contains a vector-controlled self-excited induction generator (SEIG) coupled to a wind turbine (WT) for electric power generation on the ac side. The control algorithm proposed in this paper includes three separate optimizations: two fuzzy-logic-based optimizations, which ensure that maximum energy is extracted from both the WT and the SEIG, respectively, at all operating conditions, and also the optimization of phase current harmonics through adaptive hysteresis control. In addition, a recently developed algorithm for real-time loss calculation of a hysteresis-driven power converter is utilized to quantitatively assess the power converter losses with respect to different hysteresis bandwidth settings. The performance of the proposed control algorithm is experimentally evaluated and compared with two competing algorithms which do not involve current harmonics compensation, whereas one of them also does not involve optimization of the SEIG output power. This enables evaluation of the gain in system performance due to the introduced optimizations. The evaluation and comparison are made over a wide range of wind speeds, both in steady state and under transient conditions, by using a 1.5 kW experimental setup with the DS1103 controller board (dSPACE).     

基于改进差分算法的变桨ADRC参数优化

自抗扰控制器是一类不依赖被控对象数学模型且具有较强鲁棒性及抗干扰能力的非线性控制器,已成功应用于风力发电机变桨距控制这一多变量、强耦合的非线性系统中,但自抗扰控制器也存在参数众多整定难度大这一明显缺点。现提出了通过智能算法来实现参数的自动整定。分析了改进差分进化算法的原理及步骤,并将改进差分进化算法应用到ADRC的整定过程中,实现参数的自动整定。仿真结果验证了通过改进差分进化算法自动整定ADRC参数的可行性,与传统PID控制器相比,改进差分进化算法整定后的ADRC能较好的满足风力发电机变桨控制要求,有效维持了风力发电机组输出功率的稳定性。     

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.     

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.     

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.      

智能全功率MPPT风光互补路灯控制器设计

针对一类普通风光互补路灯控制器转换能效低、稳定性差等问题,设计一种智能全功率MPPT风光互补路灯控制器.采用双MCU处理器PIC16F877A单片机为控制器核心,硬件采用模块化的设计方法,整个控制器由主控制模块、从控制模块、风力发电机智能升压MPPT模块,以及风力发电机点刹控制模块、太阳能智能升压MPPT模块、蓄电池充/放电模块、负载LED灯模块组成,在分析光伏电池和风力发电机最大功率点跟踪问题的基础上,采用风力发电机和太阳能智能全功率MPPT跟踪控制策略.最后,在实验室搭建测试平台,测试结果表明,控制器可以可靠稳定运行,跟普通控制器相比,其充电效率与能源利用能效提高22.1％,能够实现能源的最大化利用.     

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。     

H∞ position transfer and regulation for floating offshore wind turbines

This paper proposes ${\rm H}_\infty$ controller design for platform position transfer and regulation of floating offshore wind turbines. The platform movability of floating wind turbines can be utilized in mitigating the wake effect in the wind farm, thereby maximizing the wind farm''s total power capture and efficiency. The controller is designed so that aerodynamic force is adjusted to meet the three objectives simultaneously, that is, 1) to generate the desired electrical power level, 2) to achieve the desired platform position, and 3) to suppress the platform oscillation. To acquire sufficient aerodynamic force to move the heavy platform, the pitch-to-stall blade pitching strategy is taken instead of the commonly-used pitch-to-feather strategy. The desired power level is attained by the standard constant-power strategy for the generator torque, while ${\rm H}_\infty$ state-feedback control of blade pitch and nacelle yaw angles is adopted for the position regulation and platform oscillation suppression. Weighting constants for the ${\rm H}_\infty$ controller design are adjusted to take the trade-off between the position regulation accuracy and the platform motion reduction. To demonstrate the efficiency of the proposed controller, a virtual 5-MW semi-submersible wind turbine is considered. Simulation results show that the designed ${\rm H}_\infty$ controller successfully accomplishes the platform position transfer and regulation as well as the platform oscillation reduction against wind and wave disturbances, and that it outperforms a previously-proposed linear quadratic controller with an integrator.     

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.     

Design of adaptive robust guaranteed cost controller for wind power generator

According to the increasing requirement of the wind energy utilization and the dynamic stability in the variable speed variable pitch wind power generation system, a linear parameter varying (LPV) system model is established and a new adaptive robust guaranteed cost controller (AGCC) is proposed in this paper. First, the uncertain parameters of the system are estimated by using the adaptive method, then the estimated uncertain parameters and robust guaranteed cost control method are used to design a state feedback controller. The controller’s feedback gain is obtained by solving a set of linear matrix inequality (LMI) constraints, such that the controller can meet a quadratic performance evaluation criterion. The simulation results show that we can realize the goal of maximum wind energy capture in low wind speed by the optimal torque control and constant power control in high wind speed by variable pitch control with good dynamic characteristics, robustness and the ability of suppressing disturbance.     

Adjoint-based model predictive control for optimal energy extraction in waked wind farms

In this paper, a model predictive control (MPC) is proposed for wind farms to minimize wake-induced power losses. A constrained optimization problem is formulated to maximize the total power production of a wind farm. The developed controller employs a two-dimensional dynamic wind farm model to predict wake interactions in advance. An adjoint approach as an efficient tool is utilized to compute the gradient of the performance index for such a large-scale system. The wind turbine axial induction factors are considered as the control inputs to influence the overall performance by taking the wake interactions into account. A layout of a 2 × 3 wind farm is considered in this study. The parameterization of the controller is discussed in detail for a practical optimal energy extraction. The performance of the adjoint-based model predictive control (AMPC) is investigated with time-varying changes in wind direction. The simulation results show the effectiveness of the proposed approach. The computational complexity of the developed AMPC is also outlined with respect to the real time control implementation.