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.     

Optimal wind turbines placement within a distribution market environment

This paper proposes a hybrid optimization method for optimal allocation of wind turbines (WTs) that combines genetic algorithm (GA) and market-based optimal power flow (OPF). The method jointly maximizes net present value (NPV) related to WTs investment made by WTs’ developers and social welfare (SW) considering different combinations of wind generation and load demand over a year. The GA is used to choose the optimal size while the market-based OPF to determine the optimal number of WTs at each candidate bus. The stochastic nature of both load demand and wind power generation is modeled by hourly time series analysis. The effectiveness of the method is demonstrated with an 84-bus 11.4 kV radial distribution system.     

煤矿大型风机设备磨损监控系统设计

针对煤矿大型风机设备主轴磨损而影响风机工作效率的问题,在对大型风机设备磨损故障的生成原因分析后,根据系统需求设计实现了系统的硬件模块和软件模块,通过传感器电路对大型风机设备的输入输出功率、出风量、主轴转速等参数进行采集,系统软件在对数据进行综合分析后将检测结果显示在液晶屏上,实时输出大型风机设备的运行状态以及主轴磨损状况,从而保证了风机的运行效率,避免设备故障的发生;在Matlab7.0平台上完成对风机设备的主轴磨损程度进行测试,仿真时间定为3 000s,测试结果表明:该系统对风机的主轴转速的预测值与实测值接近,误差在3.19%以内,满足实际需要,具有较高的鲁棒性和运算性能,取得了令人满意的效果,有很高的推广价值。     

Reliability-based design optimization using kriging surrogates and subset simulation

The aim of the present paper is to develop a strategy for solving reliability-based design optimization (RBDO) problems that remains applicable when the performance models are expensive to evaluate. Starting with the premise that simulation-based approaches are not affordable for such problems, and that the most-probable-failure-point-based approaches do not permit to quantify the error on the estimation of the failure probability, an approach based on both metamodels and advanced simulation techniques is explored. The kriging metamodeling technique is chosen in order to surrogate the performance functions because it allows one to genuinely quantify the surrogate error. The surrogate error onto the limit-state surfaces is propagated to the failure probabilities estimates in order to provide an empirical error measure. This error is then sequentially reduced by means of a population-based adaptive refinement technique until the kriging surrogates are accurate enough for reliability analysis. This original refinement strategy makes it possible to add several observations in the design of experiments at the same time. Reliability and reliability sensitivity analyses are performed by means of the subset simulation technique for the sake of numerical efficiency. The adaptive surrogate-based strategy for reliability estimation is finally involved into a classical gradient-based optimization algorithm in order to solve the RBDO problem. The kriging surrogates are built in a so-called augmented reliability space thus making them reusable from one nested RBDO iteration to the other. The strategy is compared to other approaches available in the literature on three academic examples in the field of structural mechanics.     

Multi-disciplinary constrained optimization of wind turbines

We describe procedures for the multi-disciplinary design optimization of wind turbines, where design parameters are optimized by maximizing a merit function, subjected to constraints that translate all relevant design requirements. Evaluation of merit function and constraints is performed by running simulations with a parametric high-fidelity aero-servo-elastic model; a detailed cross-sectional structural model is used for the minimum weight constrained sizing of the rotor blade. To reduce the computational cost, the multi-disciplinary optimization is performed by a multi-stage process that first alternates between an aerodynamic shape optimization step and a structural blade optimization one, and then combines the two to yield the final optimum solution. A complete design loop can be performed using the proposed algorithm using standard desktop computing hardware in one-two days. The design procedures are implemented in a computer program and demonstrated on the optimization of multi-MW horizontal axis wind turbines and on the design of an aero-elastically scaled wind tunnel model.     

Integrated aero-structural optimization of wind turbines

The present work describes methods for the integrated aero-structural optimization of wind turbines. The goal of the algorithms is to identify the structural and aerodynamic design characteristics that achieve the minimum cost of energy for a given wind turbine configuration. Given the strong couplings that exist between aerodynamic and structural design choices, the methods are formulated so as to address both problems simultaneously in an integrated manner, resulting in tools that may help avoid suboptimal solutions or lengthy design loops.All methods considered herein use the same high fidelity multibody aeroservoelastic simulation environment and operate the design according to standard certification guidelines. The methods, however, differ in the way the optimization is conducted, realizing different tradeoffs amongst computational efficiency, generality, level of automation and overall robustness.The proposed formulations are exercised on the design of a conceptual 10 MW horizontal axis wind turbine, illustrating the main characteristics of the various methods.     

Calculation and characteristics analysis of blade pitch loads for large scale wind turbines

Based on the electric pitch system of large scale horizontal-axis wind turbines,the blade pitch loads coming mainly from centrifugal force,aerodynamic force and gravity are analyzed,and the calculation models for them are established in this paper.For illustration,a 1.2 MW wind turbine is introduced as a practical sample,and its blade pitch loads from centrifugal force,aerodynamic force and gravity are calculated and analyzed separately and synthetically.The research results showed that in the process of ro...     

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.     

Optimal blank design based on finite element method for blades of large Francis turbines

Metal pressing process that is widely used in industries has advantages over casting process for producing large Francis turbine blades from thick plates. Prior to the pressing process, blank design is firstly performed to determine flat blanks. The traditional trial and error approach is not applicable to blade design for Francis turbines that are not standard due to hydraulic characteristics of power plant sites. The rapid development of computing technology makes it possible to obtain optimal flat blanks by numerical modelling and simulation. In this paper, inverse finite element approach is investigated for blank design and an elasto-plastic model has been built using the well-known commercial software ANSYS. Numerical simulations for blade unfolding models with thick shell elements, solid elements and shell elements have given results with negligible differences. Unfolding tests with simple geometries have been carried out and the numerical results agree well with the analytical solutions. A large and thick shape of a Francis turbine blade for a hydropower plant has been successfully unfolded by inverse FE model. Sensibility analysis shows that the middle surface of the flat blank is independent of blade thickness. For ensuring the machining of the blade after the pressing process, a new contour is obtained by extending the boundary of the flat blank provided by the numerical model. This research may provide a useful tool for optimal blank design of Francis turbine blades.     

A coupled Navier-Stokes/Vortex-Panel solver for the numerical analysis of wind turbines

The present paper introduces a coupled Navier-Stokes/Vortex-Panel solver for the computational study of incompressible high Reynolds number flow around horizontal axis wind turbines. The Navier-Stokes solver is confined to the near-field around one wind turbine blade; the Vortex-Panel method accounts for the far-field of a two-bladed rotor. A robust coupling between both methods is achieved through the spanwise distribution of bound circulation determined by Stokes’ theorem. The coupled solver reduces both artificial dissipation and computational cost compared to a full-domain Navier-Stokes analysis. Results obtained for inviscid and attached viscous flow around an optimal wind turbine blade are compared to a vortex model based on strip theory. Good agreement is found between both models that serves as a validation of the coupled solver for future applications to wind turbines.     

汽车换热器风室试验台的CFD分析

为给汽车前端和发动机舱内气流数值计算提供参考依据,基于FLUENT对某汽车换热器风室试验台进行建模和数值模拟;分析风室内部空气流动状况,针对流动特征,给出风室计算流体力学（Computational Fluid Dynamics,CFD）校核的评价．网格采用四面体结构,模型中采用三维不可压的雷诺平均N．S方程,速度压力耦舍采用SIMPLE方法．空间离散格式为2阶迎风格式,时间离散格式为2阶隐式．选用realizable k-ε占模型模拟风室内部空气的湍流流动．固体壁面采用无滑移边界条件和非平衡壁面函数边界条件．模型进口采用速度入口来给定风量,出口采用压力出口．比较计算结果与试验设计标准,喷嘴压差的相对偏差范围在5％以内,基本达到对设备的精度要求,对风室设计有一定指导意义．     

Weather analysis using ensemble of connectionist learning paradigms

This paper presents a comparative analysis of different connectionist and statistical models for forecasting the weather of Vancouver, Canada. For developing the models, one year's data comprising of daily temperature and wind speed were used. A multi-layered perceptron network (MLPN) and an Elman recurrent neural network (ERNN) were trained using the one-step-secant and Levenberg–Marquardt algorithm. Radial basis function network (RBFN) was employed as an alternative to examine its applicability for weather forecasting. To ensure the effectiveness of neurocomputing techniques, the connectionist models were trained and tested using different datasets. Moreover, ensembles of the neural networks were generated by combining the MLPN, ERNN and RBFN using arithmetic mean and weighted average methods. Subsequently, performance of the connectionist models and their ensembles were compared with a well-established statistical technique. Experimental results obtained have shown RBFN produced the most accurate forecast model compared to ERNN and MLPN. Overall, the proposed ensemble approach produced the most accurate forecast, while the statistical model was relatively less accurate for the weather forecasting problem considered.     

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.     

Fixed-point digital IIR filter design using two-stage ensemble evolutionary algorithm

The research on optimal design of infinite-impulse response (IIR) filter design based on various optimization techniques, including evolutionary algorithms (EAs), has gained much attention in recent years. Previously, the parameters of digital IIR filters are encoded with floating-point representations. It is known that a fixed-point representation can effectively save computational resources and is more convenient for direct realization on hardware. Inherently, compared with the floating-point representation, the fixed-point representation would make the search space miss much useful gradient information and therefore, surely rises new challenges for continuous EAs. In this paper, we first analyze the fitness landscape properties of optimal digital IIR filter design. Based on the fitness landscape investigation, a two-stage ensemble evolutionary algorithm (TEEA) is applied to digital IIR filter design with fixed-point representation. In order to fully evaluate the performance of TEEA, we experimentally compare it with five state-of-the-art EAs on four types of digital IIR filters with different settings. Based on the experimental results, we can conclude that TEEA has higher convergence speed, better exploration, and higher success rate. In order to benchmark TEEA further, we apply it to some more difficult problems with shorter word length or higher order. We can find that TEEA can provide satisfying performance on these hard tasks as well.     

Fast simulation‐driven antenna design using response‐feature surrogates

In this article, a computationally efficient procedure for electromagnetic (EM)‐simulation‐driven design of antennas is presented. Our methodology is based on local approximation models of the antenna response, established using a set of suitably selected characteristic features rather than the entire response (such as reflection versus frequency). The approximation model is utilized to verify the level of satisfying/violating given performance requirements, and to guide the optimization process towards a better design. By exploiting the fact that the dependence of the response features on the designable parameters of the antenna of interest is simple (close to linear or quadratic), the feature‐based optimization converges faster than conventional optimization of frequency‐based EM‐simulated responses. In order to further speed up the design, coarse‐discretization simulations are utilized to estimate the feature gradients with respect to adjustable parameters of the problem at hand. The optimization algorithm is embedded in the trust‐region framework for safeguarding convergence. The proposed technique is demonstrated using two antenna examples. In both the cases, the optimum design is obtained at the computational cost corresponding to a few high‐fidelity EM antenna simulations. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:394–402, 2015.     

Modeling and robust control of a twin wind turbines structure

The control of a new structure of twin wind turbines (TWT) is presented in this paper. This new concept includes two identical wind turbines ridden on the same tower, which can pivot face the wind with no additional actuator. The motion of the arms carrying the TWT is free. The control law based on sliding mode controller is designed to track the maximum power, by controlling the rotor speed of the TWT and the yaw rotation but without yaw actuator. Finally, performances of the proposed control strategy are compared to standard proportional integral controller, for several scenarios (time varying direction or magnitude of the wind, error on the inertia of the system, …).