Active Yaw Research Articles

A novel improved actuator line method for tandem-arranged wind turbines wake analysis and its application

For a typical wind farm layout, the disturbance caused by wind turbine wakes typically exerts a significant negative impact on power generation. However, due to the insufficient reliability of existing multiple turbines wake simulations, the improved effects of wind farm layout modifications cannot be fully verified. In this paper, we employ a high-fidelity model, the Improved Actuator Line Model (I-ALM), combined with Large Eddy Simulation (LES) based on the OpenFOAM solver, to investigate the active yaw strategy of wind farms. The improvements in the ALM primarily include the Gaussian anisotropic force projection method, the integral velocity sampling method, and the enhanced nacelle-tower projection method. Firstly, we conduct numerical simulations of the NTNU "Blind Test" experimental cases using the combination of the I-ALM method and LES, mainly focusing on aerodynamic forces and wake development characteristics. Then, the proposed method is applied to the wake prediction of three NREL 5 MW tandem-arranged yaw wind turbines after benchmarking. The power generation and wake characteristics under different yaw angles is comprehensively predicted, leading to the development of a preliminary active yaw strategy (including rated state and maximum TSR state). This strategy holds significant guiding value for the accurate and efficient simulation of wind farms.

Negative effect and optimal control of in-wheel motor with inclined eccentricity on driving safety for electric vehicle

During the driving process of the in-wheel drive electric vehicle, the air gap eccentricity of the motor caused by external disturbance cannot be avoided, resulting in a negative effect on vehicle dynamics. In this paper, the negative effect of vehicle lateral dynamics caused by motor-inclined eccentricity and the corresponding control method are studied. According to the Maxwell stress tensor and the air gap permeance correction coefficient, the unbalanced radial force under the inclined eccentricity of the in-wheel motor is characterized, and the corresponding vehicle dynamics model is established. Based on the vehicle dynamics model, different steering conditions are set to explore the influence of inclined eccentricity on the negative effect of vehicle lateral dynamics. It is found that the inclined eccentricity affects the handling stability of the vehicle under normal conditions and affects the rollover stability under extreme conditions. In order to solve these problems, the integrated control strategy is formulated by using the steering and driving system of an electric vehicle, and the particle swarm optimization algorithm is introduced to optimize it. The simulation results show that the proposed integrated optimal control of active rear-wheel steering and direct yaw control can effectively alleviate the negative effect of vehicle lateral dynamics caused by the inclined eccentricity of the in-wheel motor under different working conditions.

Adaptive differential steering strategy for distributed driving unmanned ground vehicle with variable configurations based on modified localized modelling sliding mode control

When performing complex tasks such as position transfer and material transportation, the distributed driving unmanned platform with variable configurations needs to address the challenge of multi-wheel cooperative torque distribution control to achieve high-performance differential steering and enhance vehicle dynamics. The configuration change will impact the dynamic performance of the unmanned platform, posing a challenge to the performance of the existing control strategy based on mathematical model development. In order to address the aforementioned issues, this paper analyzes the impact of changes in vehicle configuration on steering gain and proposes a hierarchical adaptive differential steering strategy based on variable vehicle configurations. Firstly, the response characteristics of the yaw angle relative to the active yaw moment under the influence of changes in wheelbase and tread are analyzed. Based on this analysis, two structural modes, maneuverable and balanced, are selected. Secondly, a localized-modelling sliding mode control method with an extended state observer is proposed to estimate the desired yaw moment in the upper controller, considering the motor's execution delay. Then, the lower controller optimizes the torque of each wheel in real-time using the whale optimization algorithm. It aims to optimize tire energy dissipation and tire load rate while ensuring driving stability and achieving differential steering. Finally, through co-simulation and experiments on a scaled prototype, the reliability of the dynamics theory and the superiority of the control algorithm are validated. This optimization has led to significant improvements in the tire dissipation energy index and tire load rate index.

About control of bi-wind turbine single-point-moored floating offshore wind turbines

In the last years, several novel floating offshore wind energy concepts have appeared in the market, whose goal is the reduction of floating offshore wind energy costs. Among them, bi-wind turbine floating offshore wind turbines, which use a single floating platform to place two wind turbines, are one of the most promising ones. However, they face some challenges in their full deployment. For example, depending on the wind direction, one of the rotors could be found downstream of the other one and hence be affected by its wake. To avoid this, the turbines of this type of systems do not use a conventional active yaw mechanism, but a single-point-mooring configuration, to allow the platform rotation around the vertical axis. This way, the alignment of the rotors with the wind can be achieved, avoiding wake issues. However, as stated in the literature, at least for single-rotor wind turbines, this alignment is not straightforward and some yaw drift could appear in the platform. Therefore, the main goal of this paper is to confirm that this platform yaw drift could also arise in bi-wind turbine single-point-moored floating wind energy systems, and analyse its impact in terms of performance, while shedding some light on the performance improvements when applying new advanced control strategies.

Hunting stability control of high-speed bogie based on active yaw damper

Hunting stability is an inherent property of railway vehicles that determines the operational speed. This paper establishes a half-vehicle model with nonlinear wheel/rail equivalent conicity and interactive forces. Additionally, the dynamic performances of vehicle under various levels of wheel wear with passive suspension are compared and analyzed. Importantly, the investigation delves into the hunting stability of the vehicle system, employing both linear and nonlinear control approaches. The results demonstrate a notable reduction in critical speed during the end-worn period with a passive suspension. However, this reduction can be substantially countered through the application of active control, resulting in a significant speed increase. The implementation of stiffness control raises the frequency of limit cycles, whereas damping control serves to diminish it. Notably, an appropriate linear cubic stiffness control effectively mitigates the amplitudes of limit cycles during instances of instability. Moreover, the control strategy derived from the simplified model is extended to enhance the stability of the entire vehicle system. The research findings hold the potential to offer a promising strategy for the active control of high-speed vehicles, particularly during periods of wheel wear.

Distributed-Drive Vehicle Lateral-Stability Coordinated Control Based on Phase-Plane Stability Region

The lateral stability control of vehicles is one of the most crucial aspects of vehicle safety. This article introduces a coordinated-control strategy designed to enhance the handling stability of distributed-drive electric vehicles. The upper controller uses active front steering and direct yaw moment-control controllers designed based on sliding-mode control theory. The lower controller optimally allocates control inputs to the upper controller, considering factors such as load transfer and tire load rate. It divides the stability region by relying on the phase plane and develops a coordinated-control strategy based on the degree of deviation of the vehicle state from the stability region. The results of the simulation experiments demonstrate that the proposed control strategy effectively improves handling stability under extreme working conditions.

4P operational harmonic and blade vibration in wind turbines: A real case study of an active yaw system and a concrete tower

This study aims to comprehensively investigate the impact of mechanical loads on the performance and lifetime of wind turbines, with particular emphasis on blade vibration at the 4P operational harmonic. Experiments and advanced aeroelastic simulations are combined to assess how active yaw systems and concrete towers affect this specific vibration. Contrary to previous assumptions, field tests have shown that there is a resonance phenomenon in the blade. Specifically, the first edgewise mode of the blade resonates at the 4P frequency, which did not happen in the aeroelastic simulations. Remarkably, thorough aeroelastic simulations show that this resonance is triggered by the excitation of the Edgewise Backward Whirling mode of the rotor, which occurs at the 3P operating harmonic. This study highlights the need for accurate and precise modelling using aeroelastic simulations to reproduce the resonance phenomenon and analyse the contributing factors. A major breakthrough is the discovery that stiffening the active yaw system significantly reduces the 3P hub fixed motions, resulting in reduced blade vibration at the 4P frequency. Furthermore, the simulations show the sensitivity of the 4P vibration to different wind characteristics, providing valuable insights for the design of wind turbines in different environmental conditions.

Cooperative control strategy of trajectory tracking and driving stability for distributed-drive vehicles under extreme conditions

Active chassis dynamics control has become increasingly important in recent years for achieving high-level automated driving of intelligent electric vehicles. To improve tracking performance of the commonly-used model predictive control (MPC) in extreme scenarios, such as emergency lane changing, an improved MPC motion stability controller (MPC_ISMC) based on sliding mode variable structure active steering control and differential torque yaw control is proposed. Firstly, an MPC-based trajectory tracking controller is designed to track the desired trajectory. Subsequently, active front-wheel steering (AFS) and direct yaw moment (DYC) controllers are designed by using the backstepping sliding mode variable structure and the adaptive super-twisting sliding mode variable structure, respectively. Moreover, a cooperative control law for the AFS and the DYC is formulated by the smooth switching function and intervention intensity factor with respect to vehicle stable state and trajectory tracking error. After that, a torque distribution scheme of four wheels is optimized to achieve the additional yaw moment target. Finally, the effectiveness of the proposed cooperative control strategy is validated using a hardware-in-the-loop platform. The results indicate that the proposed controller has better performance in trajectory tracking and vehicle stability under extreme conditions compared with traditional MPC and other methods.

Wind farm optimization by active yaw control strategy using machine learning approach

ABSTRACT Recent advancements in wind farm (WF) construction have prompted designers to focus on optimizing WF performance and minimizing the negative effect of wake interference on power output. One proven technique to reduce wake influence and improve power generation is wake steering, which involves adjusting the yaw angles of wind turbines (WTs). The objective of this study is to evaluate the optimization of WF using machine learning (ML), specifically an artificial neural network (ANN) approach. In the first phase of the study, an ANN power model is selected to standardize wake loss and optimize power. In the next phase, a genetic algorithm (GA) will be employed to determine the best yaw angles for the wind direction based on data collected from the ANN power model. The ANN power model might not account for maintenance tasks around the WF. The results of the optimization demonstrate that energy is maximized between wind directions of 166° to 178°. This research achieved a .97 optimized overall power ratio, compared to non-optimized scenarios, using an optimal yaw angle technique in all directions. The findings of the study demonstrate that ANN-based optimization, combined with standardized wake degradation and suitable yaw angle direction, is an efficient method for WF optimization.

Distributed Intelligent Vehicle Path Tracking and Stability Cooperative Control

To enhance the path tracking capability and driving stability of intelligent vehicles, a controller is designed that synergizes active front wheel steering (AFS) and direct yaw moment (DYC), specifically tailored for distributed-drive electric vehicles. To address the challenge of determining the weight matrix in the linear quadratic regulator (LQR) algorithm during the path tracking design for intelligent vehicles on conventional roads, a genetic algorithm (GA)-optimized LQR path tracking controller is introduced. The 2-degree-of-freedom vehicle dynamics error model and the desired path information are established. The genetic algorithm optimization strategy, utilizing the vehicle’s lateral error, heading error, and output front wheel steering angle as the objective functions, is employed to optimally determine the weight matrices Q and R. Subsequently, the optimal front wheel steering angle control (AFS) output of the vehicle is calculated. Under extreme operating conditions, to enhance vehicle dynamics stability, while ensuring effective path tracking, the active yaw moment is crafted using the sliding mode control with a hyperbolic tangent convergence law function. The control weights of the sliding mode surface related to the center-of-mass lateral declination are adjusted based on the theory of the center-of-mass lateral declination phase diagram, and the vehicle’s target yaw moment is calculated. Validation is conducted through Matlab/Simulink and Carsim co-simulation. The results demonstrate that the genetic algorithm-optimized LQR path tracking controller enhances vehicle tracking accuracy and exhibits improved robustness under conventional road conditions. In extreme working conditions, the designed path tracking and stability cooperative controller (AFS+DYC) is implemented to enhance the vehicle’s path tracking effect, while ensuring its driving stability.

Dynamic wake field reconstruction of wind turbine through Physics-Informed Neural Network and Sparse LiDAR data

Real-time acquisition of dynamic wake field information has garnered substantial attention in the wind farm industry, as it provides a crucial data source for intelligent wind farm monitoring and control. However, the existing wind measurement technologies, such as light detection and ranging (LiDAR), are limited to sparse data point measurements. This paper explores the utilization of a Physics-Informed Neural Network (PINN) to reconstruct wind turbine wake dynamics, specifically focusing on the influence of active wind turbine yaw operation on wake evolution. The methodology involves creating a tailored loss function that combines sparse wake measurement data with the Navier-Stokes (NS) equations. More precisely, the neural network incorporates the NS equations as constraints to guide the prediction of physical quantities in the output, including downwind velocity, crosswind velocity, and pressure. Taking the dynamic wake during yawing as a case study, the proposed method showcases remarkable universality and robustness across diverse scenarios involving varying scanning angle intervals, measurement point spacings, frequencies, and noise levels. It successfully captures the dynamic trends in wake evolution during yawing and accurately forecasts the wake trajectory and deflection. Even when tested with actual wake measurement data, the method can still effectively reconstruct the flow field, indicating significant potential for the real wind farm yaw control.

Explicit hybrid MPC for the lateral stabilization of electric vehicle system

This paper presents a hybrid Model Predictive Control (hMPC) approach to improve Electric Vehicle (EV) stability when cornering or in high-risk driving conditions. By using approximations of tire force characteristics, the EV system proposed in this study is considered as a Piecewise Affine (PWA) system which is a class of Hybrid Dynamical Systems (HDSs). First, a hybrid modeling of the studied system is developed within the PWA framework. Then, the synthesis of an explicit hybrid MPC law, based on the integration of the Active Front-wheel Steering control method (AFS) and Direct Yaw moment Control method (DYC), can be established. The purpose of the PWA-MPC is to force the yaw rate and sideslip angle of the EV system to follow the given references to guarantee lateral stability. The calculation of the control laws of this proposed approach is performed offline using multiparametric programming. Finally, this paper investigates the effectiveness of the designed controller by performing a set of simulations to analyze the efficiency and robustness of the proposed approach. A comparative study is addressed in this work to demonstrate the efficiency of the proposed hybrid MPC controller compared to the conventional MPC approach which considers the linear model of the system. Another simulations are carried out to assess the robustness of the proposed control, important disturbances and uncertainties were incorporated such as the change of the additional mass, the change of terrain and the high velocity. The simulation results show that the PWA EV system model with the designed controller is able to ensure lateral stability by keeping the sideslip angle and yaw rate tracking the ideal values.

Fault diagnosis and fault tolerant control for distributed drive electric vehicles through integration of active front steering and direct yaw moment control

For the purpose of improving the lateral stability in the path following process for the distributed drive electric vehicles subject to multiple actuator/sensor faults, a new fault diagnosis and fault tolerant control method is proposed in this paper through the integration of the active front steering (AFS) and direct yaw-moment control (DYC). Firstly, considering the actuator/sensor faults and parameter uncertainties together, a Takagi–Sugeno fuzzy model is established to describe the integrated AFS/DYC system with nonlinear dynamics. Secondly, because the fault information plays a key role in the fault tolerant control, a fuzzy estimation observer is presented to diagnose actuator/sensor faults online. Thirdly, considering the high cost of the measurement sensors for lateral speed, it is hard to measure the complete state of the vehicle. Thus, in the frame of dynamic output feedback structure, a fuzzy fault tolerant control method is proposed to improve the path following performance and guarantee the dynamic stability. Finally, the hardware-in-the-loop experimental results of two typical driving maneuvers show that the proposed control method has a superior performance in the improvement of the driving stability and active safety.

Quantitative assessment on fatigue damage induced by wake effect and yaw misalignment for floating offshore wind turbines

Wake interaction between floating offshore wind turbines (FOWTs) is complex and necessitates a comprehensive assessment, which is a prerequisite for the yaw misalignment control. However, existing research on the influence of the wake effect on fatigue damage still suffers from unreliable conclusions and incomplete analysis. Consequently, we aim to precisely and comprehensively evaluate the influence of the wake effect and yaw misalignment on the fatigue damage of FOWTs. Initially, FAST.Farm is employed to holistically assess the output power, motion response, and dynamic response of two semi-submersible FOWTs arranged in series under varying yaw misalignments and wind speeds. Subsequently, the fatigue damage results of tower welds are calculated and analyzed based on the damage equivalent load and the time-varying stress, respectively. Finally, extensive comparisons and summaries are made between FOWTs under different conditions on power performance, platform motions and fatigue damage. The results indicate that active yaw control of upstream wind turbines reduces their fatigue damage of tower weld while exacerbating that of downstream wind turbines. Moreover, yaw optimization control is crucial in managing the platform motion responses.

Yaw control strategies for 20 MW multi-rotor systems

In this paper, the control of an active yaw system for a 20 MW multi-rotor wind turbine system (MRS) is considered. The MRS under investigation consists of five 4 MW turbines in two rows, which are connected to the tower via a space frame. A central yaw bearing at the height of the lower rotors is provided for active yaw. The paper aims to show that active aerodynamic yaw is possible by changing the thrust of certain rotors and should be considered for further research in the field of yawing of an MRS. Two different simulation environments are used in the paper. The MRS is modeled and simulated in Matlab/Simulink. Simulink’s own control modules are used as controllers. In the other simulation approach, the same MRS is modeled and simulated in Bladed and controlled with an external controller based on C++. The use of Bladed and Matlab/Simulink enables the plausibility and verification of the results. Matlab/Simulink is faster based of its simpler models but Bladed is more sophisticated. This paper also briefly presents the state of the art for yaw systems and yaw control of conventional single rotor wind turbines and initial research literature on yaw control of MRS.

Railway Wheelset Active Control and Stability via Higher Order Neural Units

This article investigates an unconventional approach to solving the control of lateral displacement for railway bogie wheelsets using recurrent higher order neural units (HONUs). Although studies addressing control of independently rotating wheelsets have shown promising results, they are rarely applied by railway manufacturers. Research and developments in modern bogie design are trending toward active yaw control design as an extension to conventional wheelsets mechanics, particularly for higher speeds. We investigate a model-reference architecture for active control via setpoint tracking of lateral displacement. Then, a new HONU sliding mode architecture is derived to solve convergence for zero lateral displacements in higher running speeds which is a more profoundly complex issue in maintaining minimal hunting motion. Starting from the property of nonlinear polynomial architecture of HONUs with in-parameter linearity, we derive a time-variant state-space representation via nonlinear identical decomposition. Then, an input-to-state stability (ISS) approach is applied to prove the local asymptotic convergence of the applied algorithm in each state point and the bounded-input-bounded-state stability of the entire nonlinear adaptive control loop. Using ISS theory, we also prove the global asymptotic stability of the HONU sliding mode controller for the actively controlled wheelset system. The techniques are validated by simulations and a real roller rig system.

Adaptive economic predictive control for offshore wind farm active yaw considering generation uncertainty

Inevitable model uncertainty could render active yaw control (AYC) to make suboptimal or even irrational decisions. To this end, this paper quantitatively analyzes the influential mechanisms of uncertainty on optimal yaw policy in terms of the wake model and power model. In order to address power model uncertainty that has received little attention in the AYC field, this paper proposes an efficient adaptive economic predictive control. This framework begins with the construction of a neural network-based compensator to provide online adaptive estimation of parameter uncertainty. On this basis, an adaptive economic predictive control with the economic objective of maximizing the total power is designed to increase the confidence of yaw policy in the presence of generation uncertainty, and a parallel heuristic solver is developed to allow real-time control of large-scale wind farms. Finally, extensive results based on three wind farm cases demonstrate the effectiveness and advantages of the proposed control strategy.

Bifurcation and stability analysis of a high-speed rail vehicle with active yaw dampers

Active yaw dampers (AYDs) show promise in addressing the carbody and bogie hunting issues of high-speed rail vehicles. However, the bifurcation behaviour under active control and the control law for hunting instability still require investigation. This study presents a comprehensive description of the linear control law, which can be simplified to various schemes, including skyhook damping, groundhook stiffness, modal, and blended schemes. The bifurcation behaviour under active control was analysed using a simplified lateral-dynamics-intended seven DOFs vehicle model and verified using a 3D full DOFs model in SIMPACK. Besides the well-known Hopf bifurcation, results also revealed a branching point (BP) and two new stable asymmetric equilibria. Although the asymmetric equilibria born through BP are stable, BP bifurcation should still be avoided for wheel flange contact and derailment concerns. Possible control laws are explored for two hunting scenarios: carbody hunting and bogie hunting. For carbody hunting cases, the control force can be either a linear function of bogie yaw displacement or a linear weighting function of bogie lateral and carbody roll velocity. The dominant frequency of the actuated force falls below 2 Hz, with a peak of 1.6 kN, and the allowable time delay is 70 ms. For bogie hunting cases, the control force can be a linear function of either bogie yaw displacement or velocity. In other words, both the groundhook stiffness and damping strategy are applicable. However, the actuated force contains several relatively high-frequency components (6∼13 Hz), which poses challenges to the control system and actuators, and the allowable time delay is relatively small.

Robust active yaw control for offshore wind farms using stochastic predictive control based on online adaptive scenario generation

Subject to the inherent high uncertainty of wind, the prediction for its speed and direction may be insufficiently accurate, the resulting decision actions of active yaw control (AYC) may degrade the power gain. Therefore, this paper proposes a data-driven stochastic model predictive control (SMPC) using adaptive scenario generation (ASG) for offshore wind farm AYC. First, to build precise scenarios under the nonstationary variation of wind, an adaptive method based on Gaussian mixture model (GMM) clustering is proposed to allow online scenario identification with a compact construction. Specifically, GMM is constructed offline and two online mechanisms are developed for adaptive learning ability. To immunize the power maximization of AYC against prediction error, a data-driven robust optimization strategy is presented to realize SMPC based on generated scenarios. In order to enable real-time operation for large-scale wind farms, a novel parallel marine predator algorithm (PMPA) introduced population improvement strategy is developed to solve the robust problems with a quite lower computational burden. Finally, the simulation based on realistic wind data demonstrates the adaptive learning capacity of the proposed ASG. The result shows that the SMPC can improve the power gain by an average of 2.64% compared to the baseline predictive control.

Coordinated control of AFS and DYC for electric vehicles with mechanical elastic electric wheels considering the roll effects and uncertain parameters

In order to improve the lateral stability of in-wheel motors electric vehicles (IMEV) equipped with mechanical elastic electric wheels (MEEW) under extreme conditions, this paper proposes an integral sliding mode control (ISMC) algorithm based on the coordination of active front steering (AFS) and direct yaw moment control (DYC) considering the roll effects and the uncertainty of tire cornering stiffness. Firstly, the AFS control algorithm based on adaptive integral terminal sliding mode is proposed, which uses radial-basis-function neural network (RBFNN) to adaptively approximate the integrated nonlinear unknown disturbance. Secondly, considering the roll effects and uncertain cornering stiffness, a mismatched uncertain system for integrated control of AFS and DYC is established. An integral sliding mode control algorithm based on linear matrix inequality (LMI) is designed, and the asymptotic stability of sliding mode dynamics is proved. Further, a constraint optimization algorithm is used to distribute the additional yaw moment. Then, a coordinated control strategy based on the elliptical stable region of the phase plane and the rollover index is designed to activate the integrated controller of AFS and DYC at the right time. Finally, the effectiveness of the control algorithm is verified by high-fidelity CarSim-Matlab simulations. The results show that the proposed controller can effectively and robustly ensure the vehicle lateral stability under extreme conditions.