Wind Turbine Pitch Control Research Articles

Control of a 15MW off-shore wind turbine for black-start operation

Grid forming wind turbines are crucial for achieving a 100% renewable based grid. The mechanical control of the wind turbines is usually designed for maximum power tracking operation. This paper includes the development of a wind turbine pitch controller considering the wider operational range of a grid forming wind turbine. The proposed controller tracks the optimal generation characteristic of the wind turbine when grid connected and is designed to cater for the different power levels when the wind turbine is operated in islanded mode (or grid-connected at partial power). The proposed controller does not require wind speed estimation for its operation and shows better performance than standard pitch controllers during grid-forming operation, particularly, limiting wind turbine speed excursion during block de-loading. Moreover, the proposed controller does not increase the mechanical loads of the key wind turbine elements. The performance of the proposed control is validated in a black start procedure with a full wind farm realistic real-time simulator, including detailed electrical and aeroelastic models of the wind turbine generator, wind farm and connecting HVDC link. Time and frequency domain validation using the real-time model has shown the performance of the proposed controller during black start operation.

Estimation of Hub Center Loads for Individual Pitch Control for Wind Turbines Based on Tower Loads and Machine Learning

Estimation of Hub Center Loads for Individual Pitch Control for Wind Turbines Based on Tower Loads and Machine Learning

New Adaptive Super-Twisting Extended-State Observer-Based Sliding Mode Scheme with Application to FOWT Pitch Control

This paper details the transformation of the velocity or position-tracking problem of a class of uncertain systems using finite time stability control for first-order uncertain systems. A new composite extended-state observer sliding mode (ESOSM) scheme is proposed, which includes an adaptive super-twisting-like ESO and an adaptive super-twisting controller. The adaptive super-twisting controller is implemented through a barrier function-based second-order sliding mode algorithm. To further reduce control chattering and improve control performance, the adaptive super-twisting-like ESO, which employs high-order terms in the super-twisting algorithm to accelerate convergence, is designed to observe the lumped uncertainty in real time. The advantages of the proposed scheme are verified by a numerical example and application with regard to floating offshore wind turbine (FOWT) pitch control. Compared with proportional integral (PI) and adaptive super-twisting sliding mode (ASTSM) schemes, better results are obtained in velocity tracking and fatigue load suppression. For the FOWT pitch control application, the platform roll, pitch, and yaw are decreased by 3%, 2%, and 4%, respectively, compared to the PI scheme at an average turbulent wind speed of 17 m/s and turbulence intensity of 17.27%.

Combination of fuzzy control and reinforcement learning for wind turbine pitch control

Abstract The generation of the pitch control signal in a wind turbine (WT) is not straightforward due to the nonlinear dynamics of the system and the coupling of its internal variables; in addition, they are subjected to the uncertainty that comes from the random nature of the wind. Fuzzy logic has proved useful in applications with changing system parameters or where uncertainty is relevant as in this one, but the tuning of the fuzzy logic controller (FLC) parameters is neither straightforward nor an easy task. On the other hand, reinforcement learning (RL) allows systems to automatically learn, and this capability can be exploited to tune the FLC. In this work, a WT pitch control architecture that uses RL to tune the membership functions and scale the output of a fuzzy controller is proposed. The RL strategy calculates the fuzzy controller gains in order to reduce the output power error of the WT according to the wind speed. Different reward mechanisms based on the output power error have been considered. Simulation results with different wind profiles show that this architecture performs better (123.7 W) in terms of power errors than an FLC without RL (133.2 W) or a simpler PID (208.8 W). Even more, it provides a smooth response and outperforms other hybrid controllers such as RL-PID and radial basis function neural network control.

Fuzzy-based collective pitch control for wind turbine via deep reinforcement learning

Wind turbines (WTs) have highly nonlinear and uncertain dynamics due to aerodynamic complexity, mechanical factors, and fluctuations in wind conditions. Turbulence and wind shear add complexity to modelling, especially in constant power region (region 3). Thus, an effective control design demands a deep understanding of the nonlinearities and uncertainties. This paper suggests a novel model-free reinforcement learning (RL) collective pitch angle controller to operate efficiently in region 3. The proposed controller stabilizes generator speed, maximizes power output, and minimizes fluctuations while accommodating system uncertainties, nonlinearity, and pitch limits. The disparity between WT dynamics due to wind speed perturbations and uncertainties is measured using a gap-metric criterion. The controller design adopts a deep deterministic policy gradient (DDPG) algorithm to train six agents in a medium-fidelity WT environment at different mean wind speeds to ensure the controller's robustness. Initially, imitation learning is used for efficient sample collection to fasten training convergence. Afterwards, the agent learns by interacting with the environment. After the training, the pitch control outputs from multi-trained agents are processed by a fuzzy system to have smooth transitions under different operating conditions. The resulting fuzzy DDPG (F-DDPG) controller is deployed to obtain the optimal pitch control action. The performance of the proposed F-DDPG controller is compared to the gain-scheduled PI (GSPI), Linear-Quadratic-Regulator (LQR), and single-DDPG-agent controllers. The controllers are simulated in high-fidelity onshore and offshore 5-MW WT environments using the OpenFAST/MATLAB simulation tools. The results reveal the superiority of the proposed controller in generalizing its optimal performance in different operating conditions.

Influence of Excitation by Idling Rotor on Wind Turbine Ultimate Loads in Storm Conditions

Typical large scale pitch-controlled wind turbines idle their rotors during storm conditions. The design loads of wind turbines are calculated by aeroelastic simulations under various conditions. These include grid loss and failures, which can increase rotor speed and excite the first-mode of the tower bending. In this study, the influences of self-excitation by the idling rotor on the ultimate loads in storm conditions were investigated. Aeroelastic simulations were conducted for a three-bladed 5 MW upwind turbine as an example, under steady and extreme turbulent wind conditions according to the international design standard IEC61400-1 ed.4. As a result, we confirmed that yaw misalignment increases the idling rotor speed and 6P, second order harmonics of blade passing frequency, excites the first-mode tower bending, which can generate a large load on the tower. Pitch stick can increase the rotor speed but not as noticeably as yaw error. Although no clear provisions exist in wind turbine design standards or guidelines for the self-excited vibration during wind turbine idling, these results indicate that conditions must be set that consider self-excited vibration.

Data-driven torque and pitch control of wind turbines via reinforcement learning

This paper addresses the torque and pitch control problems of wind turbines. The main contribution of this work is the development of an innovative reinforcement learning (RL)-based control method targeting wind turbine applications. Our RL-based control framework synergistically combines the advantages of deep neural networks (DNNs) and model predictive control (MPC) technologies. The proposed control strategy is data-driven, adapting to real-time changes in system dynamics and enhancing control performance and robustness. Additionally, the incorporation of an MPC structure within our design improves learning efficiency and reduces the high computational complexity typically found in deep RL algorithms. Specifically, a DNN is designed to approximate the wind turbine dynamics based on a continuously updated dataset composed of state and action measurements taken at specified sampling intervals. The real-time control policy is generated by integrating the online trained DNN into an MPC architecture. The proposed method iteratively updates the DNN and control policy in real-time to optimize performance. As a primary result of this work, the proposed method demonstrates superior robustness and control performance compared to commonly-employed MPC and other baseline wind turbine controllers in the presence of uncertainties and unexpected actuator faults. This effectiveness is showcased through simulations with a high-fidelity wind turbine simulator.

Pitch control of electrohydraulic semi-rotary-actuated wind turbine with actuator and valve fault using generalized power-based exponential rate reaching law sliding mode controller

Wind power systems comprise complex components and get exposed to harsh environments due to wind speed variations. Researchers in the domain of wind turbine system control are getting more and more attracted to applying sliding mode control, which emerges as an interesting viable option because of its ease of implementation in practice. Design of suitable nonlinear reaching laws in sliding mode control remains an active domain of research which can simultaneously offer fast convergence and improved control performance. This article develops a detailed model of a proportional valve-controlled semi-rotary-actuated wind turbine with valve deadband, valve fault, and actuator fault. Then, the work proposes a generalized power-based exponential rate reaching law-based sliding mode control for pitch control. Detailed derivation of the reaching time for the proposed sliding mode controller with the reaching law has been presented and its stability analysis has been carried out based on Lyapunov theory. Furthermore, the controller performance has been studied as a passive fault-tolerance controller. Extensive performance evaluations have been carried out using benchmark test signals and real wind data, and the superiority of the proposed controller has been demonstrated in comparison to other state-of-the-art sliding mode controls employing a variety of exponential reaching laws and recently proposed wind turbine pitch controller. The proposed generalized power-based exponential rate reaching law-based sliding mode control was able to achieve the lowest integral absolute error value of 0.0014 with real wind data in comparison to the other competing controllers considered here, that is, exponential reaching law-based sliding mode control, exponential rate reaching law-based sliding mode control, constant proportional rate reaching law-based sliding mode control, enhanced exponential reaching law-based sliding mode control, and improved exponential rate reaching law-based sliding mode control, which were able to achieve integral absolute error values of 0.0086, 0.0065, 0.0050, 0.0034, and 0.0015, respectively. Furthermore, the robustness of the proposed generalized power-based exponential rate reaching law-based sliding mode control has been aptly demonstrated with the instantaneous imposition of fault in the actuator.

Special issue on “Optimal design and operation of energy systems”

Special issue on “Optimal design and operation of energy systems”

Integral Sliding-Mode Fault-tolerant Pitch Control of Wind Turbines

In this paper, an integral sliding-mode fault-tolerant pitch control of wind turbines is presented. The proposed approach uses a fault diagnosis strategy which consists of a sliding-mode fault diagnosis observer. This observer is based on using an integral sliding-mode estimation scheme by using a suitable Lyapunov functional. Based on the previous fault diagnosis strategy, an integral sliding mode controller is designed by selecting an appropriate sliding mode surface in order to obtain the fault tolerant-control law obtained by also selecting appropriated Lyapunov functional. A wind-turbine case study is used to validate in simulation the the proposed approach.

Research on neural network wind speed prediction model based on improved sparrow algorithm optimization

The wind speed signal measured by the mechanical anemometer lags behind the wind speed at the wind wheel surface of the wind turbine in time. The wind speed reconstruction algorithm adopted by the ordinary lidar anemometer is insufficient. For the sake of enhancing the accuracy of wind speed forecast on the surface of wind turbines in wind farms for wind turbine pitch control strategies, an modified sparrow search algorithm (SSA) based on sinusoidal chaotic mapping is proposed to improve the BP neural network wind speed prediction model. According to the characteristics that lidar measure wind speed at multiple points, a multiple regression forecast model is build, and then the wind speed is predicted. Simulation tests were conducted using lidar wind measurement data from a wind farm in Xinjiang and analyzed and in contrast to a BP neural network wind speed prediction model. The experimental results indicated that the four error evaluation indicators of the Sine-SSA-BP algorithm are all smaller than the BP neural network. In contrast to the single BP neural network wind speed forecast model, sine chaotic mapping modified sparrow search algorithm optimized BP neural network has significantly improved prediction accuracy.

Adaptive Neuro-fuzzy Algorithm for Pitch Control of Variable-speed Wind Turbine

Adaptive Neuro-fuzzy Algorithm for Pitch Control of Variable-speed Wind Turbine

Error-Based Active Disturbance Rejection Control for Pitch Control of Wind Turbine by Improved Coyote Optimization Algorithm

Wind energy systems are expected to supply the majority of future generation from renewables due to the maturity of technology and the availability of energy resources. In this paper, in order to improve the ability of tracking a desired power reference at high wind speed region, a pitch control strategy based on error-based active disturbance rejection control (ADRC) approach is proposed for variable speed wind turbine system, providing a novel practically appealing control structure. The stability analysis of the proposed pitch controller based on singular perturbation theory is provided. Next, an improved coyote optimization algorithm (COA) fused with L <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rm {\acute{e}}$</tex-math></inline-formula> vy flight and Sinusoidal map is proposed to address the controller parameters tuning issue. Then, the effectiveness of the proposed solution is verified on a 1.5 MW FAST simulator taking into account both multi-step and IEC turbulence wind conditions. Finally, an analysis in frequency domain of the pitch control system with the proposed control strategy is presented.

Multivariable Model-Free Adaptive Controller Design With Differential Characteristic for Load Reduction of Wind Turbines

The asymmetric loads on wind turbines are becoming a fatal constraint factor with the increase of wind turbine size. In order to tackle this challenge, individual pitch control mechanism is introduced into the wind turbines. However, the application scopeof the traditional individual pitch control (IPC) is limited due to the complex dynamic coupling among wind turbine components, the strong nonlinearity of the pitch system and the inevitable unmodeled dynamics. Therefore, a multivariable model-free adaptive control (MIMO-DE-MFAC) strategy with differential characteristic is first proposed in this paper. Then, the proposed method is applied to the individual pitch control of the 5MW wind turbine. The simulation results from FAST demonstrate that the MIMO-DE-MFAC achieves a notable performance improvement on blade root compared with the PI. Furthermore, compared with other existing model-free adaptive control (MFAC) strategies, the proposed method not only achieves the optimal control results, but also has stronger robustness in speed-varying wind fields. Finally, the hardware-in-the-loop (HIL) experiment further verifies the reliability of the above simulation results, which provides a useful reference value for the field test.

Compound Control Strategy of Composite Wind Turbine Pitch Control Based on Differential Evolution-Fuzzy PID

Compound Control Strategy of Composite Wind Turbine Pitch Control Based on Differential Evolution-Fuzzy PID

Open-circuit Fault Riding Through of Receiving AC Terminal in HVDC Transmission System for Offshore Wind Farms

Abstract Flexible high voltage direct current (HVDC) transmission system is indispensable for long-distance and high power transmission capacity in offshore wind farms. The AC open-circuit fault of the receiving terminal is quite severe, where the power transmission might be suddenly cut down to zero. As a result, a severe over-voltage will occur on the HVDC side and modular multilevel converter (MMC) submodule capacitor. In this paper, the over-voltage suppression mechanism is put forward to augment the AC open circuit fault at the sending and receiving terminal of the MMC-HVDC system. First, the formulation mechanism of DC over-voltage in the MMC-HVDC integrated wind farm is studied. Then, three different over-voltage suppression mechanisms, namely wind turbine pitch control, crowbar control, and submodule redundancy, are put forward to suppress the aforesaid over-voltage in the MMC-HVDC system. The simulations are carried out in PSCAD based on a test case, and the simulation results are used to confirm the efficiency of the proposed overvoltage suppression techniques.

The Implementation of Fuzzy PSO-PID Adaptive Controller in Pitch Regulation for Wind Turbines Suppressing Multi-Factor Disturbances

The stability of wind turbine output is affected by multi-factor disturbances, which makes traditional proportion-integral-differential (PID) control strategy hardly achieve a satisfactory effect. Hence, a pitch control strategy based on fuzzy adaptive particle-swarm-optimization (PSO)-PID is proposed in this paper. Firstly, the models of wind turbine, pitch regulator, and permanent magnet synchronous generator (PMSG) are built. Then, the influence of various disturbances, such as blade deformation, installation errors, and external wind speed, on the wind wheel speed is analyzed. Furtherly, a wind turbine pitch control strategy is devised in detail, in which the PID parameters are initially optimized by PSO and adaptively adjusted by fuzzy controller. Simulations under gust wind and turbulent wind conditions are carried out, and the results show that compared with fuzzy PID and PID, the fuzzy adaptive PSO-PID could reduce errors of wind wheel speed and wind turbine output power effectively.

Wind turbine pitch reinforcement learning control improved by PID regulator and learning observer

Wind turbine (WT) pitch control is a challenging issue due to the non-linearities of the wind device and its complex dynamics, the coupling of the variables and the uncertainty of the environment. Reinforcement learning (RL) based control arises as a promising technique to address these problems. However, its applicability is still limited due to the slowness of the learning process. To help alleviate this drawback, in this work we present a hybrid RL-based control that combines a RL-based controller with a proportional–integral–derivative (PID) regulator, and a learning observer. The PID is beneficial during the first training episodes as the RL based control does not have any experience to learn from. The learning observer oversees the learning process by adjusting the exploration rate and the exploration window in order to reduce the oscillations during the training and improve convergence. Simulation experiments on a small real WT show how the learning significantly improves with this control architecture, speeding up the learning convergence up to 37%, and increasing the efficiency of the intelligent control strategy. The best hybrid controller reduces the error of the output power by around 41% regarding a PID regulator. Moreover, the proposed intelligent hybrid control configuration has proved more efficient than a fuzzy controller and a neuro-control strategy.

Variable Pitch Control of Wind Turbine Based on Fuzzy Feedforward and Feedback Linearization Sliding Mode

When the wind turbine operates above the rated wind speed, the pitch angle is usually adjusted to make the output power stable at the rated value. As the traditional linear controller is difficult to achieve better control effect for the large inertia and strong nonlinear wind power generation system, a variable pitch control method combining fuzzy feedforward and feedback linearization sliding mode is proposed in this study. Firstly, the nonlinear wind power model is globally linearized by input/output feedback linearization, and then the feedback controller is designed by combining with sliding mode control. The fuzzy feedforward controller can give the appropriate pitch angle according to the change of wind speed and realize dynamic feedforward compensation. The simulation results show that the proposed control method can make the output power of the wind turbine stable, and has better control accuracy and robustness.

A novel SC-PI with ANFIS compensation for wind turbine pitch control

As a common control method for blades, variable pitch technology is used to regulate the output power of the turbine in region 3. To cope with the random variation of wind speed, a novel pitch angle controller is designed in this paper. The proposed method applies a selfcoupled PI (SC-PI) to obtain the desired speed tracking performance, and employs an adaptive neuro-fuzzy inference system (ANFIS) to compensate the controller for disturbance damping capability. A contrast experiment with a baseline PI is conducted in Matlab/Simulink. The performance superiority of the proposed hybrid control method is thus confirmed.