Wind Turbine Control Research Articles

Blade aerodynamic model for thick airfoils of the series NACA00xx in pre-stall conditions

Blade aerodynamic modeling is needed for design, control, and aeroelastic studies of wind turbines. The ultimate aim of this study is to establish a blade aerodynamic model with well-quantified accuracy for thick airfoils, predominant in wind turbine blades. The study is limited to pre-stall conditions, involving only attached and trailing-edge separated flows. The account of dynamic stall will be considered in further studies. The analysis of the Glasgow University database on the family of symmetrical airfoils NACA 00xx (xx = 12,…,30) has been made by considering particularly the 2D spatiotemporal contours of the surface pressure coefficients on the suction side. Such contours provide a clear visualization of flow regime type (attached, separated or stalled) and, therefore, allow the selection of oscillatory test cases in attached or trailing-edge separated flows. The aerodynamic model of the normal force coefficient is established by improving the Beddoes–Leishman BL model. An important modification is carried on the calculation of the delayed angle of attack using the Goman–Khrabrov model, instead of the complex original procedure. There is a new aerodynamic component for simulating the trailing-edge separation. The present model, although limited to pre-stall conditions, involves ten parameters for the unsteady aerodynamic behavior. They can be obtained with the global optimization of the deviations between experimental results and model predictions. Previous optimization studies of the parameters of the BL model involve all flow regimes for test cases and do not lead to conclusive results. The parameter values obtained in the present study show a coherent and physics-expected variation with airfoil thickness that is not negligible.

A System-View Optimal Additional Active Power Control of Wind Turbines for Grid Frequency Support

A System-View Optimal Additional Active Power Control of Wind Turbines for Grid Frequency Support

Multi-objective calibration of vertical-axis wind turbine controllers: balancing aero-servo-elastic performance and noise

Abstract. Vertical-axis wind turbines (VAWTs) are considered promising solutions for urban wind energy generation due to their design, low maintenance costs, and reduced noise and visual impact compared to horizontal-axis wind turbines (HAWTs). However, deploying these turbines close to densely populated urban areas often triggers considerable local opposition to wind energy projects. Among the primary concerns raised by communities is the issue of noise emissions. Noise annoyance should be considered in the design and decision-making process to foster the social acceptance of VAWTs in urban environments. At the same time, maximising the operational efficiency of VAWTs in terms of power generation and actuation effort is equally important. This paper balances noise and aero-servo-elastic performance by formulating and solving a multi-objective optimisation problem from a controller calibration perspective. Psychoacoustic annoyance is taken as a novel indicator for the noise objective by providing a more reliable estimate of the human perception of wind turbine noise than conventional sound metrics. The computation of the psychoacoustic annoyance metric is made feasible by integrating it with an accurate and computationally efficient low-fidelity noise prediction model. For optimisation, an advanced partial-load control scheme – often used in industrial turbines – is considered, with the Kω2 controller as a baseline for comparison. Optimal solutions balancing the defined objectives are identified using a multi-criteria decision-making method (MCDM) and are subsequently assessed using a frequency-domain controller analysis framework and mid-fidelity time-domain aero-servo-elastic simulations. The MCDM results indicate the potential application of this controller in small-scale urban VAWTs to attain power gains of up to 39 % on one side and to trade off a reduction in actuation effort of up to 25 % at the cost of only a 2 % power decrease and a 6 % increase in psychoacoustic annoyance on the other side compared to the baseline. These findings confirm the flexible structure of the optimally calibrated wind speed estimator and tip-speed ratio (WSE–TSR) tracking controller, effectively balancing aero-servo-elastic performance with noise emissions and marking the first instance of integrating residential concerns into the decision-making process.

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

As global energy crises and climate change intensify, offshore wind energy, as a renewable energy source, is given more attention globally. The wind power generation system is fundamental in harnessing offshore wind energy, where the control and design significantly influence the power production performance and the production cost. As the scale of the wind power generation system expands, traditional methods are time-consuming and struggle to keep pace with the rapid development in wind power generation systems. In recent years, artificial intelligence technology has significantly increased in the research field of control and design of offshore wind power systems. In this paper, 135 highly relevant publications from mainstream databases are reviewed and systematically analyzed. On this basis, control problems for offshore wind power systems focus on wind turbine control and wind farm wake control, and design problems focus on wind turbine selection, layout optimization, and collection system design. For each field, the application of artificial intelligence technologies such as fuzzy logic, heuristic algorithms, deep learning, and reinforcement learning is comprehensively analyzed from the perspective of performing optimization. Finally, this report summarizes the status of current development in artificial intelligence technology concerning the control and design research of offshore wind power systems, and proposes potential future research trends and opportunities.

High order sliding mode observer-based fault-tolerant control scheme for DFIG in wind energy applications with model predictive controller

This research presents a novel fault-tolerant predictive power control method for a Doubly-fed induction generator (DFIG) used in wind turbine control systems. Due to the proposed control mechanism, the system can continue to function effectively despite open-circuit or short-circuit faults in the insulated-gate bipolar transistors (IGBTs) of the MPC controller. Depending on the type of problem and its location, the tolerant IGBT overcomes power oscillations and limits the power converter's potential switching states. By monitoring the optimal generator speed, wind turbine control systems strive to maximize power output. For wind turbines operating in the partial-load area, a fault-tolerant model predictive control strategy is recommended in order to achieve control goals despite disturbances, uncertainties, sensor, and actuator difficulties. A high order sliding mode observer (HOSMO) is used to evaluate both the actual states and sensor-faults at the same time. A high order sliding mode (HOSM) control strategy based on the MPC controller is used to regulate the speed of wind turbines in order to harness the wind's maximum power.

A reduced order modeling-based machine learning approach for wind turbine wake flow estimation from sparse sensor measurements

A comprehensive understanding of wind turbine wake characteristics is vital, particularly in the context of expanding large offshore wind farms. Existing wake measurement techniques provide only spatially sparse wake measurement data, limiting their utility in precise wind turbine design and control. This paper introduces a data-driven approach that combines proper orthogonal decomposition (POD) with machine learning (ML) techniques, designing a Reduced Order Modeling-based Wake Flow Estimation (ROM-WFE) framework. This framework establishes a nonlinear mapping between sensor measurements and low-dimensional POD coefficients. Two distinct sensor placements, wall-mounted and wake-mounted, are investigated for real measurement scenarios. The results highlight the effectiveness of the proposed wake flow estimation method in reconstructing a complete flow field from exceptionally sparse sensor data, with both wall-mounted and wake-mounted strategies, exhibiting promising results with maximum relative errors of 6.37% and 4.51%, respectively. From the reliability assessments considering various configurations of POD modes and sensor numbers, the ROM-WFE framework demonstrates its capability to estimate wake flow effectively, offering a cost-effective tool for practical applications. Furthermore, the framework maintains accuracy even with high-noise and low-frequency data, demonstrating robustness and generalization. This method significantly contributes to wind turbine wake prediction controller design, promising accurate and robust wake flow field estimation, potentially revolutionizing active wake control and enhancing wind farm operational efficiency.

Interval reservoir computing: theory and case studies

The time series data in many applications, for example, wind power and vehicle trajectory, show significant uncertainty. Using a single prediction value of wind power as feedback information for wind turbine control or unit commitment is not enough since the uncertainty of the prediction is not described. This paper addresses the uncertainty issue in time series data forecasting by proposing the novel interval reservoir computing method. The proposed interval reservoir computing can capture the underlying evolution of the stochastic dynamical system for time series data using the recurrent neural network (RNN). On the other hand, by formulating a chance-constrained optimization problem, interval reservoir computing outputs a set of parameters in the RNN, which maps to an interval of prediction values. The capacity of the interval is the smallest one satisfying the condition that the probability of having a prediction inside the interval is lower than the required level. The scenario approach solves the formulated chance-constrained optimization problem. We implemented an experimental data-based validation to evaluate the proposed method. The validation results show that the proposed interval reservoir computing can give a tight interval of time series data forecasting values for wind power and traffic trajectory. In addition, the confidence probability over the feasibility goes to 1 very quickly as the sample number increases.

Global vibration control of offshore wind turbines with a flexible monopile foundation using a pendulum-tuned mass damper: Risk mitigation and performance incrementation

This study investigates an optimized design approach for a passive-adaptive pendulum-tuned mass damper (PTMD). The PTMD is used to mitigate structural vibrations in offshore wind turbines (OWTs) with flexible monopile foundations considering Pile-Soil Interaction (PSI). The model OWT is based on the 5-MW design proposed by the National Renewable Energy Laboratory (NREL). The PSI incorporates a pile that is modeled as beam–column elements supported by nonlinear springs and accounts for lateral loads (p-y curves) and axial loads (t-z and Q-z curves) at nodal points. Wind and wave spectra estimation, as well as hydrodynamic and aerodynamic load analysis, are performed using a bespoke MATLAB® program operated in conjunction with an ANSYS® 3-D finite element global model. The resultant peak response at the OWT hub was evaluated through power spectral density (PSD) analysis. Optimal design choices for PTMD parameters are obtained via parametric analysis, which assesses the dynamic response in the along and across directions at the hub and stress at the tower base. A systematic representation considers the uncertainties stemming from the geometric and mechanical properties of the tower, environmental loads, and rotating blade dynamics. To address these uncertainties, a Monte Carlo simulation is employed to assess the structural risk via the Performance-Based Wind Engineering (PBWE) framework. Consequently, the probabilistic evaluation of structural response, foundation stresses, and power production assists in the optimal design process of PTMDs that mitigate the global structural vibrations of OWTs.

Integral Backstepping Sliding Mode Control for Maximizing the Power Production of Wind Turbines

Wind turbine control has attracted increasing attention, driven in part by evolving challenges due to the growing size and complexity of wind turbines. Addressing these challenges and maximizing wind turbine power production requires the application of advanced nonlinear control methods. Sliding Mode Control (SMC) has emerged as a promising approach in this context. Recent studies have explored the integration of an integral term with SMC, called I-SMC. This technique has been shown to result in system responses that exhibit chattering phenomena with noticeable state errors. This study aimed to address these issues through the introduction of a novel controller known as Integral Backstepping SMC (IB-SMC). This study demonstrated that IBSMC not only ensured the stability of wind turbines but also outperformed other control strategies, even in the presence of disturbances of approximately 30% of the rated electromagnetic torque. To validate the effectiveness of the proposed controller, extensive simulation tests were carried out using MATLAB /Simulink software to evaluate the controller's responsiveness to rapid changes in conditions, as well as its robustness and overall performance. A comparison was carried out between the IBSMC and previous SMCs to evaluate their ability to reduce steady-state error and chattering.

Coordinated control and energy management combined with cyberattack identification in multi‐microgrid integrated with hybrid renewable‐storage

AbstractA comprehensive model is developed for coordinated control of voltage‐frequency‐inertia and identifying multiple cyberattacks simultaneously in two microgrids (MGs). The MGs are integrated with solar units, Wind turbine (WT), hybrid supercapacitor‐battery, and fuelcell. The MGs are modelled and controlled for operation under both an island and connected states. In the proposed method, a data centre is designed in which all the electrical and control signals related to the solar, wind, hybrid supercapacitor‐battery, and Fuel cell (FC) are collected, evaluated, and matched. The data centre comprises the following blocks: voltage‐frequency control, inertia control of WT, and identification of false data injection (FDI) cyberattacks on frequency, power, power/frequency, and voltage. The technique used in this article to identify FDI attacks is based on the real‐time method coupled with logical comparisons conducted in the time domain. This methodology provides prompt and precise detection, allowing for timely preventive measures and strategic responses. After FDI attacks occur, the implemented control system effectively manages and regulates the voltage and frequency at the desired levels, efficiently differentiating between ordinary functioning, faulty states, and potential cyber‐attacks. The unhealthy MG can transfer its load to the healthy MG for safety reasons. The healthy MG is then connected to the external grid and the synchronisation conditions are checked by the proposed control system. The results of the non‐linear simulation performed in MATLAB‐Simulink software confirm that the proposed model successfully operates and controls all resources (i.e. solar/wind/battery/FC), regulates the voltage/frequency under various loading conditions, and identifies FDI cyberattacks.

Active Edgewise Blade Damping Control of Large Wind Turbines by Using the Pitch Controller and an Interval Observer

Large wind turbines have typically poorly damped structures. Hence, the absence of damping leads to aeroelastic oscillations, and the operational rotor speed can approach the critical rotor speed. By using damping injection, the control system can actively introduce some additional damping. In the present work, a control approach to reduce oscillations of the rotor blades in the edgewise direction is proposed. The concept is based on the damping injection mechanism, and an additional level of safety is obtained by introducing the Dynamic Safety Margin (DSM) in the control law. The feedback control scheme requires some unmeasurable variables. This aspect is covered by using an interval observer. The control approach is tested by using simulations on a high-definition model implemented in an aeroservoelastic code. Simulation results are very satisfactory and promising for future experiments using hardware-in-the-loop equipment.

Coordinating Demand Response and Wind Turbine Controls for Alleviating the First and Second Frequency Dips in Wind-Integrated Power Grids

This paper introduces a method employing demand response (DR) and wind turbine (WT) to decrease the first and second frequency dips in power systems with the massive proliferation of wind resources. Inverter air conditioners (IACs) are utilized to participate in the DR program. The buildings' thermal model and the IACs' electrical model are integrated to characterize a relation between power system frequency and IACs regulation power. An adaptive delay controller (ADC) is presented to capture the command communication delays from the DR controller to IACs. In addition, a controller is suggested to adopt wind power plants in system frequency regulation. This controller rises the wind plant output power for some seconds and returns it, following frequency recovery, to the initial power. The recovery period causes the second frequency dip, in response to which, a method is introduced to identify the moment of the second dip and alleviate it via DR.

Metamaterial-based vibration control for offshore wind turbines operating under multiple hazard excitation forces

Wind energy holds growing investment perspectives due to its renewable and sustainable energy production potential. The harvesting capability of wind turbines is directly related to their size. Therefore, those wind energy generators still encounter challenges related to excessive vibrations aggravated by environmental factors, compromising energy generation and resulting in fatigue damage and downtime. Various vibration control strategies have been explored to address these issues, with passive control emerging as a preferred choice due to its ease of manufacture, cost-effectiveness, and simple installation. Nevertheless, the limitations of these controls lie in their substantial size and weight associated with optimal installation positions for achieving a good performance. Recently, mechanical metamaterials have demonstrated tremendous advancements in efficiency regarding band isolation and vibration mitigation, coupled with enhanced manufacturing possibilities. Hence, this paper proposes a novel approach to control the vibration of wind turbines employing mechanical metamaterials in their design. A spectral approach is used to model the metamaterial turbine and its interaction with external excitations, such as wind, waves, and blade rotation. The numerical results show the efficiency of the metamaterial turbine by its tunable stopband features and vibration amplitude reduction under multiple hazard excitations, emphasising its superior suppressing vibrations compared to conventional tuned mass damper. Thus, the proposed wind turbine metastructures offer a compelling combination of effective vibration attenuation while reducing dynamic resonators’ overall stiffness, mass, and size. The outcome advances wind turbine design and energy technology for reliable wind energy production.

Adaptive integral sliding mode MPPT control for wind turbines with fixed‐time convergence

AbstractIn the context of increasing renewable energy penetration and the imperative for efficient utilization, this paper explores the application of maximum power point tracking (MPPT) control for wind turbines. The objective is to optimize wind power generation by maximizing energy capture efficiency while addressing challenges associated with behind‐the‐meter renewables, load flexibility, and encouraging prosumers' participation. This paper proposes an adaptive integral sliding mode MPPT control strategy for wind turbines in the presence of external disturbance and model uncertainties. A new fixed‐time integral sliding mode manifold is constructed, such that the singularity problem can be avoided in the controller design without using any piecewise continuous functions. Then, an adaptive fixed‐time MPPT controller is developed to ensure that the tracking error can converge to a small neighbourhood around zero in fixed time, where the upper bound of the convergence time can be pre‐set based on actual engineering needs by selecting appropriate parameters. By designing update law, no prior knowledge on the upper bound of the lumped uncertainties is required anymore in advance. Stability analysis of the closed‐loop system is conducted using Lyapunov stability theory, and the effectiveness of the proposed strategy is validated through numerical simulations.

Cyber-Resilient Converter Control System for Doubly Fed Induction Generator-Based Wind Turbine Generators

As wind turbine generator systems become more common in the modern power grid, the question of how to adequately protect them from cyber criminals has become a major theme in the development of new control systems. As such, artificial intelligence (AI) and machine learning (ML) algorithms have become major contributors to preventing, detecting, and mitigating cyber-attacks in the power system. In their current state, wind turbine generator systems are woefully unprepared for a coordinated and sophisticated cyber attack. With the implementation of the internet-of-things (IoT) devices in the power control network, cyber risks have increased exponentially. The literature shows the impact analysis and exploring detection techniques for cyber attacks on the wind turbine generator systems; however, almost no work on the mitigation of the adverse effects of cyber attacks on the wind turbine control systems has been reported. To overcome these limitations, this paper proposes implementing an AI-based converter controller, i.e., a multi-agent deep deterministic policy gradient (DDPG) method that can mitigate any adverse effects that communication delays or bad data could have on a grid-connected doubly fed induction generator (DFIG)-based wind turbine generator or wind farm. The performance of the proposed DDPG controller has been compared with that of a variable proportional–integral (VPI) control-based mitigation method. The proposed technique has been simulated and validated utilizing the MATLAB/Simulink software, version R2023A, to demonstrate the effectiveness of the proposed method. Also, the performance of the proposed DDPG method is better than that of the VPI method in mitigating the adverse impacts of cyber attacks on wind generator systems, which is validated by the plots and the root mean square error table found in the results section.

Robust control of wind turbines to reduce wind power fluctuation

AbstractThe wind power generation system of a 5 MW horizontal axis wind turbine has a high wind energy conversion efficiency. The proportion of installed capacity in practical production applications is increasing year on year, so that the stability of its operation becomes a central factor in determining the productivity of the wind farm in question. This paper takes a 5 MW wind turbine as the research object and proposes a parameter‐adaptive robust model predictive control method to achieve self‐optimization of controller parameters through a Bayesian optimization approach. A robust model predictive control strategy, aiming to reduce the power fluctuation while maximizing the power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A Bayesian algorithm is used in this paper to optimize the parameters of the controller. Moreover, wind speeds are simulated using TurbSim for different turbulence intensities of 5%, 10%, and 15% turbulence. Finally, the robust model predictive control toolbox in MATLAB is designed and simulated. The results show that the operational instability of the wind energy system is overcome. Meanwhile, the robustness of the wind energy system operation is improved compared to the traditional model predictive control approach.

A novel control strategy of a variable-speed doubly-fed-induction-generator-based wind energy conversion system

Abstract This paper aims to address the issue of control of a variable-speed wind turbine based on doubly-fed induction generators. In this work, an effort is made to extract the maximum efficiency from a doubly-fed induction generator-based variable-speed wind turbine by controlling the rotor current. In the first step, a maximum power point tracking technique is used to extract the maximum power from the turbine. Then a stator-flux-oriented vector control strategy is employed to control the rotor-side current. Subsequently, a grid voltage vector-oriented control strategy is used to control the grid-side system of the grid-connected generator. Considering the nonlinearity and parameter uncertainty of the system, an active disturbance rejection controller with a sliding-mode-based extended-state observer is developed for the above-mentioned control strategies. Furthermore, the stability of the controller is tested and the performance of the controller is compared with the classical proportional–integral controller based on disturbance rejection, robustness and tracking capability in a highly non-linear wind speed variation scenario. Modelling, control and comparison are conducted in the MATLAB®/Simulink® environment. Finally, a real-time hardware set-up is presented using the dSPACE ds-1104 R&D processing board to validate the control scheme. From the result of the experiments, it is seen that the proposed controller takes 10–15 control cycles to settle to its steady-state values, depending on the control loop, whereas the conventional proportional–integral controller takes 60–75 control cycles. As a result, the settling time for the proposed control scheme is shorter than that of the proportional–integral controller.

Modelling and Power Control of Wind Turbine Driving DFIG connected to the Utility Grid

In this paper, the modelling and power transfer control of the variable speed wind energy system are presented. Firstly, a Maximum Power Point Tracking (MPPT) method is developed in order to maximize the energy generation of wind system based on doubly fed induction generator (DFIG). Then, the control of both Rotor Side Converter (RSC) and Grid Side Converter (GSC) is studied and the design of a multivariable selective filter is presented. Finally, simulation of a 850 kW doubly fed induction generator wind system was performed in the Matlab / Simulink / SimPowerSystems environment

Effect of Rain on Vertical Axis Wind Turbines

Results are presented that demonstrate that rain will have a significant effect on the output of a vertical axis wind turbine. The experiments were carried out in the climatic wind tunnel at the University of Nottingham where water was sprayed into the wind tunnel to simulate several rainfall rates. The rain had the effect of increasing the drag, slowing the rotational speed of the wind turbine and decreasing the Power for the equivalent wind speed. The increasing in the drag has the additional effect of decreasing the optimal coefficient of performance as the rainfall rate is increased. Similar studies in airfoil performance in the rain have shown that the increase in drag and decrease in lift is related to the chord length of the airfoil and so could potentially be larger for larger turbine blades. This could have an effect on the control strategy necessary for controlling wind turbine performance and will need to be studied further.

Virtual inertia support based frequency and dynamic voltage control for grid-connected SPMSG-based wind turbine

The Challenge to stabilize the grid frequency increases with the increment of renewable energy resources. System inertia/frequency control is a significant concern for maintaining system stability with a fast response time during the load transition. This manuscript proposes surface-mounted permanent magnet synchronous generator (SPMSG) based wind energy conversion system (WECS) with a frequency support DC-link controlling loop and a converter protective DC-link voltage-controller frequency support system. To achieve power exchange, the frequency control system (FCS) for the SPMSG-based wind turbine supports the grid-side virtual inertia. The DC-link voltage is disturbed during the load transition to maintain the frequency, while the electrolytic capacitor requires extra care regarding the DC bus voltage. This manuscript incorporates the FCS system with a supercapacitor and dynamic voltage limiter to avoid additional care of the DC bus voltage. A detailed analysis of system frequency with the additional load increment, normal connected load decrement, and various fault scenarios has been done. The proposed system is compared with the existing virtual inertia support (VIS). The simulation results show that the proposed frequency control system-based VIS efficiently limits the frequency deviations & DC-link voltage. The results are verified in the OPAL-RT 4510 real-time simulator environment to ensure the efficacy of the proposed system.