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.     

A novel fault diagnosis technique for wind turbine gearbox

The gearbox is one of the most important parts of a mechanical equipment. The importance of fault diagnosis in rotating machineries for preventing catastrophic accidents and ensuring adequate maintenance has received considerable attention. In this study, a fault diagnosis method based on gearbox vibration signal monitoring is used to differentiate the signal characteristics of different working conditions and improve the accuracy of diagnosis. The time-domain sequence approximate entropy (ApEn) adaptive strategy is used to propose a wind turbine intelligent fault diagnosis algorithm based on a wavelet packet transform (WPT) filter and a cross-validated particle swarm optimized (CPSO) kernel extreme learning machine (KELM). First, the correlation between the parameter requirements of the intelligent diagnosis system and the system complexity analysis is analyzed. Then, the parameters related to the wavelet filter is determined by calculating the ApEn of the time-domain sequence. Finally, a compact wind turbine gearbox test bench is constructed and tested to validate the proposed ApEn-WPT+CPSO-KELM to identify gearbox-related faults for verification. Results show that the proposed ApEn-WPT+CPSO-KELM method can accurately identify four states of the wind turbine gearbox.     

Structural design optimization of wind turbine towers

This paper describes several optimization models for the design of a typical wind turbine tower structure. The main tower body is considered to be built from uniform segments where the effective design variables are chosen to be the cross-sectional area, radius of gyration and height of each segment. The nacelle/rotor combination is regarded as a rigid non-rotating mass attached at the top of the tower. Five optimization strategies are developed and tested. The last one concerning reduction of vibration level by direct maximization of the system natural frequencies works very well and has shown excellent results for both tower alone and the combined tower/rotor model. Extensive computer experimentation has shown that global optimality is attainable from the proposed discretized model and a new mathematical concept is given for the exact placement of the system frequencies. The normal mode method is applied to obtain forced response for different types of excitations. The optimization problem is formulated as a nonlinear mathematical programming problem solved by the interior penalty function technique. Finally, the model is applied to the design of a 100-kW horizontal axis wind turbine (ERDA-NASA MOD-0). It has succeeded in arriving at the optimum solutions showing significant improvements in the overall system performance as compared with a reference or baseline design.     

Multi-model predictive control for wind turbine operation under meandering wake of upstream turbines

In wind farm operation, the performance and loads of downstream turbines are heavily influenced by the wake of the upstream turbines. Furthermore, the actual wake is more challenging due to the dynamic phenomenon of wake meandering, i.e. the turbine wake often demonstrates dynamic shift over time. To deal with the time-varying characteristics of wake meandering, a multiple model predictive control (MMPC) scheme is applied to the individual pitch control (IPC) based load reduction. The coherence function in the spectral method is used to generate the stochastic wind profile including wake meandering at upstream turbine, and a simplified wake meandering model is developed to emulate the trajectory of the wake center at downstream turbine. The Larsen wake model and Gaussian distribution of wake deficit are applied for composing wind profiles across the rotor of downstream turbines. A set of MMPC controllers are designed based on different linearized state-space models, and are applied in a smooth switching manner. Simulation results show significant reduction in the variation of both rotor speed and blade-root flapwise bending moment using the MMPC based IPC by including the wake meandering, as compared to a benchmark PI controller designed by NREL.     

风力发电系统传感器故障诊断

针对非线性风力发电系统,提出了一种基于滑模观测器的传感器故障诊断方法.基于考虑传感器加性故障的非线性动态模型,利用T--S模糊理论建立风力发电系统全局T--S模型,设计模糊T--S系统滑模故障观测器,产生对故障具有敏感性的残差,实现故障检测.通过等价输出控制方法来维持滑模运动,直接获取故障信息,重构传感器故障.最后以三叶片水平轴风力发电系统为例,仿真验证了该方法的有效性与可靠性.     

Distribution recurrence plots and measures: Effective signal analysis tools for fault diagnosis of wind turbine drivetrain system

Extracting sensitive information from vibration signal has become a frequently adopted way in fault diagnosis. However, most previous methods fragmented the relationship between quantification and visualization analysis, which affects the interpretability, accuracy and comprehensiveness of the extracted information. To this end, this paper proposes distribution recurrence plots (DRP) and measures (DRM) to realize the unity of visualization and quantification analysis of the signals. Specifically, DRP is a novel feature graphical representation method following the thought of symbolic dynamics. Derived from DRP, DRM is developed containing four quantifiers for extracting comprehensive fault features that allows a multiclass support vector machine (SVM) to identify the fault types of wind turbine drivetrain system (WTDS). Specially in DRM, pattern entropy is a newly designed quantifier by considering pattern distribution to obtained more accurate quantitative representation of the signals. Using simulated data, DRP and DRM are validated to reveal the intrinsic structural changes for different dynamic systems and robustness to noise. Applications on wind turbine gearbox illustrate that the proposed method has favorable diagnosis performance and stability compared with other competitors. This approach is easy to interpret, is robust to noise, and has a low computational burden, becoming viable for WTDS fault diagnosis.     

Real-time economic nonlinear model predictive control for wind turbine control

Nonlinear model predictive control (NMPC) is a strong candidate to handle the control challenges emerging in the modern wind energy industry. Recent research suggested that wind turbine (WT) control based on economic NMPC (ENMPC) can improve the closed-loop performance and simplify the task of controller design when compared to a classical NMPC approach. This paper establishes a formal relationship between the ENMPC controller and the classic NMPC approach, and compares empirically their closed-loop nominal behaviour and performance. The robustness of the performance is assessed for an inaccurate modelling of the tower fore-aft main frequency. Additionally, though a perfect wind preview is assumed here, the effect of having a limited horizon of preview of the wind speed via the LIght Detection And Ranging (LIDAR) sensor is investigated. Finally, this paper provides new algorithmic solutions for deploying ENMPC for WT control, and report improved computational times.     

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.     

Passivity-based control for variable speed constant frequency operation of a DFIG wind turbine

A standard passivity based control for a double fed induction generator of a wind turbine is presented. The control problem is posed as a variable speed constant frequency operation with the aim to maximise the generated electric power. The controller is designed in such a way that the dual control objective, unity power factor in the stator side and speed tracking in the mechanical port, are satisfied guaranteeing internal stability. The proposed scheme is the first attempt to approach the speed tracking operation from the energy dissipation (passivity) perspective. Simulation results show good performance of the control scheme for wind speeds in different operating regimes.     

Routing in wireless sensor networks for wind turbine monitoring

Smart wireless sensor devices are rapidly emerging as key enablers of the next evolution in wind turbine monitoring. The potential for in-situ monitoring of turbine elements, employing methodologies that are not possible with existing wired technology, make it possible to attain new levels of granularity and autonomy in the monitoring of these structures. Wireless sensor devices are limited in terms of communication by the range of their radio modules and, thus, need to form networks in order to transfer data from distant points. Routing protocols are primary enablers of such ad hoc wireless sensor networks and these require the implementation of reliable and energy-efficient mechanisms to maximize network reliability and availability. Existing routing protocols cannot be directly applied to the monitoring of wind turbines without addressing the unique context and operational characteristics of these structures in multi-hop wireless communication. This work identifies the potential effects associated with the operation, environment and structure of wind turbines in wireless sensor network multi-hop communication, and proposes and evaluates a reliable routing protocol for wireless sensor networks employed in these domains.     

风电机组齿轮箱的多变量时间序列故障预警

针对风电机组故障预警中，原始动态时间规整（DTW）算法无法有效度量风电机组多变量时间序列数据之间距离的问题，提出一种基于犹豫模糊集的动态时间规整（HFS-DTW）算法。该算法是原始DTW算法的一种扩展算法，可对单变量和多变量时间序列数据进行距离度量，且精度与速度较原始DTW算法更优。以子时间序列相似度距离为目标函数，使用帝国竞争算法（ICA）优化了HFS-DTW算法中的子序列长度和步距参数。算例研究表明与仅DTW算法和非参数最优的HFS-DTW算法相对比，参数最优的HFS-DTW可挖掘更多的多维特征点信息，输出的多维特征点相似序列具有更丰富细节；且基于所提算法可提前10天预警风电机组齿轮箱故障。     

双馈型机组风电场三相电网短路故障暂态特性分析

风电场三相电网短路故障暂态特性是研究大型风电场和电网之间相互关系的问题之一.首先基于机理分析,建立了机组和电网数学模型.然后利用Matlab/Simulink仿真环境和机组参数,建立了风电场全系统的仿真模型.最后通过对双馈型机组风电场三相电网短路故障下的数字仿真,得到了风电场和机组各关键参数的响应特性,并着重对它们的暂态特性进行了分析.所建立的模型和仿真结果为今后进一步研究风电场与电网之间的相互影响提供了理论基础.     

Robust nonlinear controller design of wind turbine with doubly fed induction generator by using Hamiltonian energy approach

Based on Hamiltonian energy theory, this paper proposes a robust nonlinear controller for the wind turbine with doubly fed induction generator (DFIG), such that the closed-loop system can achieve its stability. Furthermore, in the presence of disturbances, the closed-loop system is finite-gain L2 stable by the Hamiltonian controller. The Hamiltonian energy approach provides us a physical insight and gives a new way to the controller design. The simulation results illustrate that the proposed method is effective and has its advantage.     

CRONE control based anti-icing/deicing system for wind turbine blades

This paper presents an application of CRONE control, a robust control based on fractional order differentiation, to an anti-icing/deicing system for wind turbine blades. The deicing system uses an electrically conductive polymer paint applied on the blade at ice formation areas. The voltage applied to the paint leads to its heating. Based on a temperature measure provided by various sensors placed on the blade, a control system can thus be used to prevent the blade icing up during wind turbine operation or to deice the blade after a rest time. To design the control system, a thermal model of the blade with the paint was developed and its associated parameters were identified using tests in a climatic wind tunnel. A CRONE controller was then designed and its performance evaluated both on blade subparts in the climatic wind tunnel and on a floor-standing real blade.     

风力发电机输出信号仿真检测和分析系统

建立了一种基于LabVIEW软件平台的风机信号仿真检测和分析系统。系统使用LabVIEW的信号发生器仿真风机的输出信号,在软件中设置FIR滤波器对风机输出信号进行调理,并通过离散傅里叶变换和自相关函数分别对信号做频域和时域分析。结果表明,本系统可以准确的仿真风机信号的发生、检测和分析过程。     

某大兆瓦风电机组主机架多阶段多目标优化

为解决大兆瓦风电机组主机架研发难度大、周期长的问题,采用贯穿主机架全生命周期的多阶段多目标优化方法进行研发设计.在概念设计阶段侧重于获得主机架初始构型,以机架材料分布为设计变量,以材料体积为约束条件,以各工况极限强度为目标进行拓扑优化;在详细设计阶段侧重于机架的轻量化,以主机架结构尺寸为设计变量,以疲劳性能为约束条件,...     

On addressing noise constraints in the design of wind turbine blades

Power production from wind energy has been increasing over the past decades, with more areas being used as wind farms and larger wind turbines (WTs) being built. With this development, awareness of the impact of wind energy on the environment and on human health has also raised. There has been a large interest in developing fast turnaround WT blade design frameworks, capable of predicting both aerodynamic and aeroacoustic performance to handle ever stricter noise criteria constraints dictated by site or local authorities. In this work, a blade element momentum theory model is used to predict the aerodynamic performance of a wind turbine, coupled to an empirical aeroacoustic noise model and boundary layer corrections. The aeroacoustic prediction code developed was validated against measurement data of the AOC 15/50 WT and included in an optimization framework using a genetic algorithm. The blade shape was parametrized using NURBS curves for the cross sectional airfoil shapes and Bézier curves for the twist and chord distributions, totaling up to 62 design variables. Two multi-objective optimization cases, both single- and multi-operating point, were performed. Optimal solutions selected from the Pareto fronts are discussed in detail. These solutions ranged from an increase in annual energy production of 15 % to a reduction in noise levels of 9.8 %. It was demonstrated that substantial noise reduction could be obtained at an expense of a minor aerodynamic penalty.     

变速风力发电机故障观测器的设计和应用

针对模型包含未知非线性函数的变速风力发电机故障诊断问题,采用反卷法和最小二乘支持向量机对未知非线性函数进行辨识,将辨识结果作为补偿项加入比例高阶积分观测器中,消除未知非线性项对故障诊断精度的影响,建立了基于最小二乘支持向量机非线性项辩识模型的比例高阶积分故障观测器。李雅普诺夫函数证明该故障观测器的稳定性,仿真实验表明该故障观测器可以准确、快速、有效地诊断变速风力发电机故障。     

大型风力发电机变桨控制系统设计

变桨距风力发电机已成为风力发电机组的主要研究和发展方向,文章以大型风力发电机组的变桨距系统为研究对象,通过对硬件系统的功能描述,设计了变桨系统的总硬件电路和伺服电路。在此基础上,为实现硬件电路的良好调节作用,对风机启动后变桨程序和调功程序的软件流程图作了详细设计。经过以上的软硬件设计,满足了大型风力发电机组变桨的控制要求。在实际应用中,获得了良好控制效果。     

基于Adams的风机机械动力学分析

介绍风机组成和工作原理,建立风机三维模型,用Adams对风机机构进行动态仿真分析,得到关键运动部件的运动规律曲线和动力学分析曲线,结果表明Adams对复杂机构的仿真准确可靠.得到叶片与基轴之间的速度、角速度和力矩分析曲线,零部件间冲击和扰动产生的动力学响应,为风机减振降噪的优化设计提供参考.