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.     

自适应控制在变速风力发电系统中的应用

针对变速风力发电系统提出了一种自适应反馈线性化控制器。该控制器通过对涡轮轴转矩的自适应估算,将其作为参考转矩提供给磁场定向控制的鼠笼式异步电机。异步电机通过变速箱与涡轮轴相连接。反馈线性化控制器用于保持涡轮转速与用户自定义的辅助输入量的线性关系。控制器的参考转速是风速的函数,它的选择随风力状况的变化而变化,目的是为了获取最大风能。仿真结果表明,该控制器能够获取最大可用风能,控制效果良好。     

An adaptive observer for wind velocity using a new torque model of a wind turbine

In this paper, a new mechanical torque model of a wind system is presented, which is used to design an adaptive observer, whose goal is to estimate the wind speed and the mechanical torque in a wind turbine. This adaptive observer allows to obtain, in real time, the turbine maximum power point with wind turbine power control purposes without requiring the precise knowledge of the performance coefficient curve or look-up tables, that are currently used in most control schemes. The new model is compared with a heuristic model and validated with an experimental system.     

Wind turbine integrated structural and LPV control design for improved closed-loop performance

An iterative redesign algorithm is proposed to integrate the design of the structural parameters and a linear parameter-varying (LPV) controller for a three-bladed horizontal-axis wind turbine. The LPV controller is designed for an eighth-order lumped model of the wind turbine consisting of blades, drive-train and the tower. The lumped model response is matched with detailed open-loop numerical simulations using the Fatigue, Aerodynamics, Structures and Turbulence (FAST) code. The controller is scheduled in real-time based on the mean wind speed to account for the varying system dynamics. The objective is to track the operating trajectory meanwhile minimise the H _∞ performance index from the wind turbulence to the controlled output vector consisting of pitch angle, blade tip deflection, and the generator speed and torque. Sensitivity analysis of the closed-loop performance index with respect to the structural parameters of the system is examined. The integrated design problem is formulated as an iterative sequential controller/structure redesign to obtain the structural parameters and controller matrices corresponding to a local optimal performance index. Each step of the iterative procedure is formulated as a linear matrix inequality (LMI) optimisation problem that can be solved efficiently using available LMI solvers. The evolution of the structural parameters and performance index through the integrated design is illustrated. The FAST closed-loop simulations for two selected designs with the smallest values of the performance index demonstrate the improved performance of the overall system through the integrated structure/control redesign in both minimising the effect of the wind disturbance on the generator output power, and reducing the structural loads on the wind turbine.     

风力发电机组的变论域自适应模糊控制

建立了变速变浆距风力发电机组的简化模型。在此基础上，将变论域自适应模糊控制应用到风力发电机组的转速和浆距控制系统中，改善风力发电机组的风能捕获性能。变论域自适应模糊控制器在保持规则形式不变的前提下。论域随着误差的变化而变化。这种控制器不但具有经典模糊控制的优点，如不需要精确的数学模型，产生非线性控制动作，良好的动态性能等．而且具有较高的控制精度。仿真结果证明该方法改善了风力发电机组的控制性能。     

风电机组变桨距模糊遗传控制算法研究

为提高额定风速以上风力发电机组发电机转速和输出功率的稳定性,基于风电机组的运行特性,建立了风电机组变桨距控制仿真模型;针对遗传算法收敛速度慢的缺点,采用模糊遗传算法对PID控制器参数进行整定。仿真结果表明,基于模糊遗传的控制器不仅提高了遗传算法的收敛速度,且在动态性能及系统稳定性方面均优于遗传算法控制器。     

Neuro–fuzzy modeling tools for estimation of torque in Savonius rotor wind turbine

In the present paper, the ability and accuracy of an adaptive neuro–fuzzy inference system (ANFIS) has been investigated for dynamic modeling of wind turbine Savonius rotor. The main objective of this research is to predict torque performance as a function of the angular position of turbine. In order to better understanding the present technique, the dynamic performance modeling of a Savonius rotor is an important consideration for the wind turbine design procedure. It could be difficult to derive the exact mathematical derivation for the input–output relationships because of the complexity of the design algorithm. In order to show the best fitted algorithm, an extensive comparison test was applied on the ANFIS (adaptive neuro–fuzzy inference system), FIS (fuzzy inference system), and RBF (radial basis function). Resulting from the extensive comparison test, the ANFIS procedure yields very accurate results in comparison with two alternate procedures. The results show that there is an excellent agreement between the testing data (not used in training) and estimated data, with average errors very low. Also FIS with threshold 0.05 and the trained ANFIS are able to accurately capture the non-linear dynamics of torque even for a new condition that has not been used in the training process (testing data). For the sake of comparison, the results of the proposed ANFIS model is compared with those of the RBF model, as well. For implementation of the present technique, the Matlab codes and related instructions are efficiently used, respectively.     

Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 2: Application examples

This paper is part two of a two part series. The originality of part one was the proposal of a novelty approach for wind turbine supervisory control and data acquisition (SCADA) data mining for condition monitoring purposes. The novelty concerned the usage of adaptive neuro-fuzzy interference system (ANFIS) models in this context and the application of a proposed procedure to a wide range of different SCADA signals. The applicability of the set up ANFIS models for anomaly detection was proven by the achieved performance of the models. In combination with the fuzzy interference system (FIS) proposed the prediction errors provide information about the condition of the monitored components.Part two presents application examples illustrating the efficiency of the proposed method. The work is based on continuously measured wind turbine SCADA data from 18 modern type pitch regulated wind turbines of the 2 MW class covering a period of 35 months. Several real life faults and issues in this data are analyzed and evaluated by the condition monitoring system (CMS) and the results presented. It is shown that SCADA data contain crucial information for wind turbine operators worth extracting. Using full signal reconstruction (FSRC) adaptive neuro-fuzzy interference system (ANFIS) normal behavior models (NBM) in combination with fuzzy logic (FL) a setup is developed for data mining of this information. A high degree of automation can be achieved. It is shown that FL rules established with a fault at one turbine can be applied to diagnose similar faults at other turbines automatically via the CMS proposed. A further focus in this paper lies in the process of rule optimization and adoption, allowing the expert to implement the gained knowledge in fault analysis. The fault types diagnosed here are: (1) a hydraulic oil leakage; (2) cooling system filter obstructions; (3) converter fan malfunctions; (4) anemometer offsets and (5) turbine controller malfunctions. Moreover, the graphical user interface (GUI) developed to access, analyze and visualize the data and results is presented.     

Robust Wind Speed Estimation and Control of Variable Speed Wind Turbines

In this work, a robust control scheme for variable speed wind turbine system that incorporates a doubly feed induction generator is described. The sliding mode controller is designed in order to track the optimum wind turbine speed value that produces the maximum power extraction for different wind speed values. A robust sliding mode observer for the aerodynamic torque is also proposed in order to avoid the wind speed sensors in the control scheme. The controller uses the estimated aerodynamic torque in order to calculate the reference value for the wind turbine speed. Another sliding mode control is also proposed in order to maintain the dc‐link voltage constant regardless of the direction of the rotor power flow. The stability analysis of the proposed controller under disturbances and parameter uncertainties is provided using the Lyapunov stability theory. Finally, the simulation results show that the proposed control scheme provides a high‐performance turbine speed control, in order to obtain the maximum wind power generation, and a high‐performance dc‐link regulation in the presence of system uncertainties.     

基于有效风速估计与预测的风电机组自适应最大风能跟踪控制

针对如何在有效风速未知情况下实现风电机组最大风能跟踪(MPPT)的问题,本文使用支持向量回归(SVR)和自适应控制原理,提出基于有效风速估计与预测的自适应MPPT控制方案.首先,使用机组的历史运行数据,训练得到基于SVR的风速估计与预测模型,为MPPT控制提供实时参考输入.其次,结合在线学习估计器(OLA)和减小转矩增益(DTG)控制原理,设计自适应MPPT控制器,该控制器能够较好应对系统未知动态特性和干扰,且能降低传动链载荷.最后,使用李雅普诺夫原理证明闭环系统所有信号都是有界的.仿真结果表明本文提出的方法能够获得良好的MPPT效果,进而提高机组产能.     

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.     

Adaptive control of Hammerstein–Wiener nonlinear systems

The Hammerstein–Wiener model is a block-oriented model, having a linear dynamic block sandwiched by two static nonlinear blocks. This note develops an adaptive controller for a special form of Hammerstein–Wiener nonlinear systems which are parameterized by the key-term separation principle. The adaptive control law and recursive parameter estimation are updated by the use of internal variable estimations. By modeling the errors due to the estimation of internal variables, we establish convergence and stability properties. Theoretical results show that parameter estimation convergence and closed-loop system stability can be guaranteed under sufficient condition. From a qualitative analysis of the sufficient condition, we introduce an adaptive weighted factor to improve the performance of the adaptive controller. Numerical examples are given to confirm the results in this paper.     

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.     

风力发电系统的Hamilton建模及其无速度传感器控制

本文研究了基于端口受控Hamilton(port-controlled Hamiltonian, PCH)模型的永磁同步风力发电系统的无速度传感器控制问题. 首先根据风力发电系统本身的物理结构特性, 建立其端口受控Hamilton结构模型, 然后基于此模型设计了系统的全维状态观测器, 得到系统的速度估计, 从而实现了系统无速度传感器控制. 在控制器设计中还考虑了系统存在参数不确定情况下的自适应控制问题, 以及在控制器设计中用状态量的估值直接替代不可测状态量时存在的误差问题. 最后仿真实验证明了控制器的有效性.     

非线性时滞系统的稳定自适应控制

针对模型不确定性的连续时间时滞系统，提出了一种新的神经网络自适应控制。系统的辨识模型是由神经网络和系统的已知信息组合构成，在此基础上，建立时滞系统的预测模型。基于神经网络预测模型的自适应控制器能够实现期望轨线的跟踪，理论上证明了闭环系统的稳定性。连续搅拌釜式反应器仿真结果表明了该控制方案的有效性。     

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.     

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.     

Adaptive control of a class of slowly time varying systems with modeling uncertainties

In a recent work, a new linear adaptive controller based on certainty-equivalence and backstepping design, which promises a level of transient and asymptotic performance comparable to that of the tuning functions adaptive backstepping controller without using high order nonlinearities, was proposed for linear time invariant systems. The proposal was supplemented with robustness and performance analysis in the presence of modeling uncertainties. In this note, the same idea is used to develop a new linear adaptive controller for slowly time varying systems with modeling uncertainties. The new adaptive control scheme guarantees robustness with respect to modeling errors via normalizing damping, parameter projection, and static normalization. Use of normalizing damping is essential in protecting the "linearity" of the system, which plays a key role in reaching the stability and robustness results.     

一种自适应CMAC神经元网络控制器及其在水轮机调速器中的应用

提出了一种自适应CMAC神经元网络控制器的结构. 该控制器的核心是一个两维的存贮区间, 它采用一个参考模型和直接自适应律来获得在线训练信号, 而相应存贮单元的更新采用一阶学习律. 最后以水轮机调速器仿真实验系统来检验它的控制性能, 并与普通的PID控制器比较, 结果证明, 该控制器有较强的学习能力及较强的鲁棒性.     

The art of modeling and simulation of induction generator in wind generation applications using high-order model

Both fixed-speed squirrel-cage induction generators and variable-speed doubly fed induction generators are used in wind turbine generation technology. Modeling and simulation of induction machines using vector computing technique in Matlab/Simulink provides an efficient approach for further research on wind generation system integration and control. In this paper, the vector computing technique is applied in modeling and simulation of induction machines. Free acceleration of squirrel-cage induction generator, active power and reactive power control of DFIGs in a power system as well as inter-area oscillation damping control are demonstrated using the proposed model. The modeling approach in Matlab/Simulink makes controller design and simulation verification effective.