Variable-speed Variable-pitch Wind Turbines Research Articles

Study on Typical Aerodynamic Faults of Variable Pitch Wind Turbine

The variable-speed variable-pitch wind turbine is an important part of China's power and energy systems, and is also the main conversion form of wind energy utilization. Aiming at the problem that the traditional blade element-momentum theory cannot achieve the modeling and simulation of wind turbine plane wind unbalance caused by wind shear and tower shadow effect, the modeling and simulation numerical calculation method of aerodynamic load of wind turbine actuation disk with different blade wind unbalance pitch angles is proposed, This method can derive the analytical expressions for solving the key variables of load calculation, axial induction factor and tangential induction factor, and realize the aerodynamic load solution. At the same time, it is also verified that the characteristic vibration component of 3 times the low speed shaft rotation frequency (3P) is a typical dynamic load feature of tower shadow effect, and is also the most important aerodynamic load fluctuation feature of variable-speed variable-pitch wind turbine, However, under normal conditions, the wind shear effect has little effect on the wind turbine load fluctuation.

Nonlinear control of a class of non-affine variable-speed variable-pitch wind turbines with radial-basis function neural networks

Due to complicated dynamics, wind turbines’ governing equations are subject to uncertainties and unknown disturbance sources. Despite uncertainties and disturbance sources, the paper’s focus is to design an adaptive controller that enables trajectory-tracking with a zero-converging tracking error. As the main result of a zero tracking error, the turbine can operate at maximum power efficiency. In addition, novel Lyapunov functions are proposed introducing auxiliary adaptive terms to result in closed-loop asymptotic stability in the presence of a non-affine controller input, uncertainties, and unknown disturbance sources. Considering the turbine dynamics, one can divide the wind turbine control problem into torque and pitch control phases. For addressing the nonlinearities and uncertainties of the dynamics in each phase, RBF neural networks are utilized to develop novel control and adaptive laws. To address the non-affine dynamics stemming from the pitch angle, a neural network alongside the implicit function and mean-value theorems are utilized to transform the dynamics into the control affine form. Several auxiliary adaptive variables are proposed in the transformation procedure, leading to closed-loop asymptotic stability. Moreover, using the Lyapunov stability analysis, closed-loop asymptotic stability is obtained for each phase. In the end, simulation results are presented to verify the analytical results where the proposed controller’s performance is compared to that of an existing method in different scenarios. The proposed controller’s simulation results suggest dramatic improvement over those of the existing method in both trajectory-tracking and required control action.

Smoothing the output power of a wind energy conversion system using a hybrid nonlinear pitch angle controller

Wind energy has been developed and is widely used as a clean and renewable form of energy. Among the existing variety of wind turbines, variable-speed variable-pitch wind turbines have become popular owing to their variable output power capability. In this study, a hybrid control strategy is proposed to implement pitch angle control. A new nonlinear hybrid control approach based on the Adaptive Neuro-Fuzzy Inference System and fuzzy logic control is proposed to regulate the pitch angle and maintain the captured mechanical energy at the rated value. In the controller, the reference value of the pitch angle is predicted by the Adaptive Neuro-Fuzzy Inference System according to the wind speed and the blade tip speed ratio. A proposed fuzzy logic controller provides feedback based on the captured power to modify the pitch angle in real time. The effectiveness of the proposed hybrid pitch angle control method was verified on a 5 MW offshore wind turbine under two different wind conditions using MATLAB/Simulink. The simulation results showed that fluctuations in rotor speed were dramatically mitigated, and the captured mechanical power was always near the rated value as compared with the performance when using the Adaptive Neuro-Fuzzy Inference System alone. The variation rate of power was 0.18% when the proposed controller was employed, whereas it was 2.93% when only an Adaptive Neuro-Fuzzy Inference System was used.

Comparison between Improved PID and LQ Multimodel Optimal Controllers for Variable-Speed Wind Turbines

In this research study, a comparison between two improved multimodel optimal controllers PID and LQ for two bases with 4 and 8 models is proposed for a variable speed variable pitch wind turbine. This turbine uses a blade pitch angle and electromagnetic torque control to achieve objectives. But we are particularly interested in minimizing the fluctuations of the electrical power and the turbine rotational speed to allow the participation of the wind turbines in the frequency regulation. A 2 MW wind turbine is considered to clarify the achieved good performances by the proposed strategy while presenting and discussing the simulation results of the two multimodel optimal controllers PID and LQ. These results show better performance for improved LQ multimodel optimal controller than the improved PID multimodel optimal controller, and with the base of the eight models compared to the base of the four models.

Design and Verification of the LQR Controller Based on Fuzzy Logic for Large Wind Turbine

In this study, a linear quadratic regulator based on the fuzzy logic (LQRF) control algorithm for a variable-speed variable-pitch wind turbine was designed. In addition, to verify the optimum performance of the controller, simulations and wind tunnel tests were conducted. In the simulation, the performances of the proportional-integral (PI) and LQRF algorithms were compared in the transition region and the rated power region. In the wind tunnel test, the applicability of the LQRF algorithm was verified by comparing it with the conventional PI algorithms. The results showed that when compared with the PI control, the proposed LQRF control reduced the tower vibration by up to 12.50% depending on the operating region. Furthermore, the power deviation was reduced by 38.93%. These tests confirmed that the proposed LQRF control increases the power performance and structural stability of wind turbines compared with conventional PI controls.

Scheduling of [formula omitted] controllers in horizontal axis wind turbines

Safe pitch angle-based pole–zero–gain scheduling, and controller blending of H∞ controllers for a variable-speed variable-pitch wind turbine in the full load region are introduced in this paper. The design methodology ensures proper model reduction and modification, canonical controller realization, and cancellation of the hidden coupling terms that emerge from these scheduling procedures to maintain the stability of the closed–loop dynamics during wind turbine operation. In contrast to previous multi-model designs that either involve scheduling of complex transfer functions or depend on less-reliable wind speed estimations, in the presented framework only a portion of the controller is scheduled against the pitch angle. This approach markedly facilitates the implementation of the technique for typical H∞ synthesis controllers which are of high order. To provide further insight into the stability properties of the proposed control system, sufficient conditions for the stability of the closed–loop system are derived using the frozen-input theory. Moreover, in order to obtain a reliable uncertainty estimation, a time-marching simulation is utilized which requires only some basic characteristics of the wind turbine. Finally, explicit requirements for the pole–zero–gain scheduling and controller blending are derived and their performance is investigated using an HIL setup that connects validated FAST simulation codes to the actual control PLC of a 2MW industrial wind turbine.

Robust control design for an industrial wind turbine with HIL simulations

This paper describes the design and implementation of a H∞-based robust controller for a commercial 2MW variable-speed variable-pitch wind turbine. The single controller is able to deliver specification performance for the entire full load region. Various aspects of modeling and design procedures including reduced order model validation, torsional mode damping of drivetrain and uncertainty estimation are explained in detail. To make the design more reliable and industrially attractive, simple routines for the derivation of required weighting functions are introduced. To investigate the benefits of the suggested design, its closed-loop performance is compared with the wind turbine’s baseline controller and an alternative state–space controller proposed in the literature. This is first evaluated using a FAST-based simulator under IEC-61400 compatible scenarios. It is then verified by a real-life hardware in the loop simulator using an industrial PLC.

Hybrid modelling and predictive control of utility-scale variable-speed variable-pitch wind turbines

Wind energy has proven to be the highest reliable source of renewables due to the maturity of the technology. Wind turbine (WT) systems are complex systems with undergoing development process that requires innovative methods of design and control. In this paper, WTs are studied through hybrid systems framework. Hybrid models of WT are extracted with representable dynamics from nonlinear complex design code. The WT model is formulated into a mixed-logical dynamical model (MLD) and a piecewise affine (PWA) model. Then, a receding horizon control strategy is applied to WT hybrid model resulting in a hybrid model predictive control (HMPC) with mixed-integer programming (MIP) problem. The performance of the proposed controller is compared against the baseline controller within a simulation environment for the National Renewable Energy Laboratory (NREL) 5MW benchmark WT as a case study. The analysis and investigation of HMPC highlight its capability as a potential tool for exploiting the control objectives of WT systems.

Variable Pitch Active Disturbance Rejection Control of Wind Turbines Based on BP Neural Network PID

When wind speeds are above the rated speed of variable speed variable pitch wind turbines, pitch angles are changed to keep output powers and rotor speeds at their rated values. For wind turbines with nonlinear and complex structure, conventional PID variable pitch controller is difficult to achieve precise control. In this paper, a variable pitch controller combining back-propagation(BP) neural network with PID (BP-PID) is proposed. By real-time detecting the deviation of the rotor speeds, the BP neural network with self-learning and weighting coefficient correction capability is used to adjust the PID parameters online and further to achieve the optimal combination of the PID parameters. Considering various uncertain disturbances and parameter changes on the mechanical components of the wind turbines, an active disturbance rejection pitch controller of the wind turbines is designed based on BP-PID algorithm. Combined with a tracking differentiator, an extended state observer (ESO) is employed to observe the state and disturbance of the system. In addition, in order to compensate the BP-PID variable pitch controller, nonlinear state error feedback control laws are designed by configuring nonlinear structures according to the state deviation between the extended state observer and the tracking differentiator. The simulation results show that the variable pitch active disturbance rejection control (ADRC) based on BP-PID can effectively estimate the system states and disturbances. And the proposed controller has good dynamic performance and strong robustness.

Adaptive control design and implementation for collective pitch in wind energy conversion systems

This paper introduces a discrete-time L1 adaptive controller design for collective pitch control for variable speed variable pitch wind turbines. In this control algorithm, the blades’ pitch angles are regulated to manipulate the generator speed and generated power during high speeds wind airflow. The main merits of the proposed controller are its robustness against the wind turbine model uncertainties, guaranteeing the closed loop system stability and performance, and applicability during implementation in real life. Commonly used industrial gain-scheduled PI controller is used to be compared with the proposed controller. The proposed scheme is simulated using a 5-MW offshore turbine model and the obtained results illustrates the superior performance of the proposed controller. Further, a reduced scale wind turbine emulator is used to provide hardware in the loop to test the implementation of the proposed controller. The controller is implemented using laboratory equipment to demonstrate the feasibility of the controller for real time applications. The obtained experimental results validate the closed loop system performance enhancement due to replacing the traditional PI controller with the proposed design.

Gain-scheduled control of wind turbine exploiting inexact wind speed measurement for full operating range

This note tackles the problem of variable-speed variable-pitch wind turbine control in the whole wind speed range. Gain-scheduled (GS) control strategy is exploited to provide maximum power point tracking operation and to achieve effective pitch angle regulation. The wind turbine is modeled in the form of a linear parameter varying (LPV) system using wind speed as the scheduling parameter to cope with the varying system dynamics. The wind speed measurement errors are systematically considered in the design procedure. Modeling error is handled through employing an induced L2-gain performance criterion. The proposed method results a common gain-scheduled controller for the whole operating region. A benchmark example illustrates the advantages of the presented method.

Comparative Analysis for Interval Modeling Algorithms of Wind Turbine Power Curve

Considering the stochastic uncertainty of wind power generation, joint estimation of wind turbine power curve (WTPC) in point and interval forms is carefully studied in this paper. Firstly, inducing factors of uncertain data and outliers types in measured data are analyzed to show necessity of uncertainty modeling of WTPC. Then, theoretical principles of different methods for WTPC modeling such as Gaussian Bin method, kernel density estimation (KDE), conditional kernel density estimation (CKDE), conditional probability via Copula, Gaussian process regression (GPR) and relevance vector machine (RVM), are seriously straightened up. Their theoretical essences are discussed and compared in depth. Subsequently, evaluation indexes for point and interval estimations of WTPC are defined to quantify modeling performance. Finally, using measured data from actual 1.5MW variable-speed variable-pitch (VSVP) wind turbines, simulations are executed to show diversities of these methods and their features are discussed from different views. Meanwhile, their potential application scenarios and usage strategies are carefully analyzed, including wind power prediction and operational performance evaluation of wind turbine in an infinite-time-horizon form or a sliding-window form. It is helpful to enhance development of WTPC modeling techniques.

Continuous-time multi-model predictive control of variable-speed variable-pitch wind turbines

ABSTRACTThe control objective for wind turbine control systems includes maximum power tracking, speed and power regulation and minimisation of mechanical loads. The operating region of a wind turbine is a function of the incoming wind speed and the control objective in each operating region differ significantly from the other. Therefore, a linear controller designed based on a model obtained at one operating point cannot guarantee stability and satisfactory performance across the whole operating regime of the turbine. In this paper, a continuous-time multi-model predictive controller is proposed for variable-speed variable-pitch wind turbine systems. Four controllers were designed using linearised models obtained at 4, 8, 11 and . A Bayesian probability inference function was used to make the transition between these controllers based on the errors between the system outputs and each operating point. Simulation studies based on a benchmark 5 MW wind turbine were used to demonstrate the performance of the proposed controller.

Feedforward Feedback Pitch Control for Wind Turbine Based on Feedback Linearization with Sliding Mode and Fuzzy PID Algorithm

After exceeding rated power, variable speed variable pitch wind turbines need to keep output powers at rated value by adopting pitch angles. With the typical nonlinear characteristics of the wind turbines, it is difficult to control accurately by conventional linear controller. Though feedback control can realize the stability of the system, it is effective only when deviations are produced. As such, feedforward control can be applied to compensate the time-delay produced by feedback control. Accordingly, we propose a compound control strategy that combines feedback controller with feedforward controller in this paper. In feedback loop, we adopt fuzzy algorithm to adjust the parameters of PID controller. Furthermore, to overcome large variation of input wind speed, variable universe theory is proposed to optimize fuzzy algorithm. In feedforward loop, we propose feedback linearization to address nonlinear problem. Furthermore, sliding mode algorithm is supplied to improve the robustness of feedback linearization. Therefore, feedforward loop can efficiently compensate time-delay deficiency of wind turbine systems. Simulation results show that the proposed controller can enhance the control accuracy and robustness of the system.

Active Fault Diagnosis on a Hydraulic Pitch System Based on Frequency-Domain Identification

The blade pitch system is a critical subsystem of variable-speed variable-pitch wind turbines that is characterized by a high failure rate. This paper addresses the fault detection and isolation (FDI) of a blade pitch system with hydraulic actuators. Focus is placed on incipient multiplicative faults, namely hydraulic oil contamination with water and air, bearing damage resulting in increased friction, and drop of the supply pressure of the hydraulic pump. An active model-based FDI approach is considered, where changes in the operating conditions (i.e., mean wind speed and turbulence intensity) are accounted through the identification of a linear parameter-varying model for the pitch actuators. Frequency-domain estimators are used to identify continuous-time models in a user-defined frequency band, which facilitates the design of the FDI algorithm. Besides, robustness with respect to noise in measurements and stochastic nonlinear distortions is ensured by estimating confidence bounds on the parameters used for FDI. The approach is thoroughly validated on a wind turbine simulator based on the FAST software that includes a detailed physical model of the hydraulic pitch system. This paper presents the design methodology and validation results for the proposed FDI approach. We show that an appropriate design of the excitation signal used for active fault detection allows an early fault diagnosis (except for oil contamination with water) while ensuring a short experiment duration and an acceptable impact on the wind turbine operation.

A Novel Constant Output Powers Compound Control Strategy for Variable-Speed Variable-Pitch Wind Turbines

The output power of wind turbine should be maintained at rated value when wind speed exceeds rated speed.However, the wind turbine is a system with large nonlinearity and time delay. Therefore, it is difficult for conventional linear controllers to realize effective control. In order to solve this problem, a combination of feedforward control with feedback control is developed. In feedback loop, feedback linearization theory is adopted to overcome the nonlinear features, and sliding mode algorithm is employed to improve the robustness of the system. On the other hand, in feedforward loop, a cerebellar model articulation controller is implemented to approximate the system. Furthermore, an improved credit assignment method is used to adjust the weight of the network. Together these procedures improve the effectiveness of the algorithm. Through this method, not only can the stability of the nonlinear model be ensured, but also the control lag caused by the time-delayed system can be compensated. Simulation results show that the feedforward-feedback compound control algorithm can both keep the output power at rated value and improve dynamic response speed, reduce adjustment time, has small steady-state error.

Control of variable speed variable pitch wind turbine based on a disturbance observer

In this paper, a novel sliding mode controller based on disturbance observer (DOB) to optimize the efficiency of variable speed variable pitch (VSVP) wind turbine is developed and analyzed. Due to the highly nonlinearity of the VSVP system, the model is linearly processed to obtain the state space model of the system. Then, a conventional sliding mode controller is designed and a DOB is added to estimate wind speed. The proposed control strategy can successfully deal with the random nature of wind speed, the nonlinearity of VSVP system, the uncertainty of parameters and external disturbance. Via adding the observer to the sliding mode controller, it can greatly reduce the chattering produced by the sliding mode switching gain. The simulation results show that the proposed control system has the effectiveness and robustness.

Coordinated Control Strategies for a Permanent Magnet Synchronous Generator Based Wind Energy Conversion System

In this paper, a novel co-ordinated hybrid maximum power point tracking (MPPT)-pitch angle based on a radial basis function network (RBFN) is proposed for a variable speed variable pitch wind turbine. The proposed controller is used to maximise output power when the wind speed is low and optimise the power when the wind speed is high. The proposed controller provides robustness to the nonlinear characteristic of wind speed. It uses wind speed, generator speed, and generator power as input variables and utilises the duty cycle and the reference pitch angle as the output control variables. The duty cycle is used to control the converter so as to maximise the power output and the reference pitch angle is used to control the generator speed in order to control the generator output power in the above rated wind speed region. The effectiveness of the proposed controller was verified using MATLAB/Simulink software.

Comparative Analysis of Decoupling Control Methodologies and H∞ Multivariable Robust Control for Variable-Speed, Variable-Pitch Wind Turbines: Application to a Lab-Scale Wind Turbine

This work is focused on the improvement of variable-speed variable-pitch wind turbine performance by means of its control structure. This kind of systems can be considered as multivariable nonlinear processes subjected to undesired interactions between variables and presenting different dynamics at different operational zones. This interaction level and the dynamics uncertainties complicate the control system design. The aim of this work is developing multivariable controllers that cope with such problems. The study shows the applicability of different decoupling methodologies and provides a comparison with a H∞ controller, which is an appropriate strategy to cope with uncertainties. The methodologies have been tested in simulation and verified experimentally in a lab-scale wind turbine. It is demonstrated that the wind turbine presents more interaction at the transition zone. Then, this operational point is used as the nominal one for the controller designs. At this point, decoupling controllers obtain perfect decoupling while the H∞ control presents important interaction in the generated power loop. On the other hand, they are slightly surpassed by the robust design at other points, where perfect decoupling is not achieved. However, decoupling controllers are easier to design and implement, and specifically dynamic simplified decoupling achieve the best global response. Then, it is concluded that the proposed methodologies can be considered for implantation in industrial wind turbines to improve their performance.

Power maximization of variable-speed variable-pitch wind turbines using passive adaptive neural fault tolerant control

Power maximization has always been a practical consideration in wind turbines. The question of how to address optimal power capture, especially when the system dynamics are nonlinear and the actuators are subject to unknown faults, is significant. This paper studies the control methodology for variable-speed variable-pitch wind turbines including the effects of uncertain nonlinear dynamics, system fault uncertainties, and unknown external disturbances. The nonlinear model of the wind turbine is presented, and the problem of maximizing extracted energy is formulated by designing the optimal desired states. With the known system, a model-based nonlinear controller is designed; then, to handle uncertainties, the unknown nonlinearities of the wind turbine are estimated by utilizing radial basis function neural networks. The adaptive neural fault tolerant control is designed passively to be robust on model uncertainties, disturbances including wind speed and model noises, and completely unknown actuator faults including generator torque and pitch actuator torque. The Lyapunov direct method is employed to prove that the closed-loop system is uniformly bounded. Simulation studies are performed to verify the effectiveness of the proposed method.