Wind Turbine Control Research Articles

Vibration control and energy harvesting of offshore wind turbines installed with TMDI under dynamical loading

This study investigates the performance of multiple tuned mass-damper-inerter with multiple electromagnetic motor (TMDI-EM) for the dual objectives of enhancing energy harvesting and dynamic performance in offshore wind turbines (OWTs). OWTs, inherently susceptible to dynamic sensitivity under lateral loads such as wind and waves, necessitate effective solutions for vibration mitigation while harnessing energy from dynamic excitations. The TMDI-EM configuration integrates an electromagnetic motor (EM) as a shunt damper between a secondary mass and an inerter element in series. The scalable inertance of the inerter element allows for adaptability in practical device implementations. Genetic Algorithm (GA) is employed to find optimum parameters for the TMDI-EM system. The research focuses on the dynamic response of OWTs under real-world conditions, where wind and wave forces act as correlated random excitations. Parametric analyses assess the available energy for harvesting at the EM and the displacement variance of the OWT structure as the inertance of the TMDI-EM varies. The study employs methods to optimize the TMDI-EM parameters using Genetic Algorithm, ensuring the system's optimal performance for both enhanced energy harvesting and dynamic response. The findings indicate that lightweight TMDI-EMs, representing only 1.5 % of the OWT structure's mass, demonstrate improved vibration suppression and energy harvesting performance with increasing inertance. This study contributes into the potential integration of TMDI-EMs to enhance the dynamic performance and energy harvesting capabilities of offshore wind turbines under dynamical loading conditions. The use of Genetic Algorithm ensures the identification of optimal parameters for the TMDI-EM system, enhancing its practical applicability.

An Experimental Performance Assessment of a Passively Controlled Wind Turbine Blade Concept: Part A—Isotropic Materials

This paper is the first part of a two-part series, which presents preliminary findings on a novel flexible curved wind turbine blade designed for passive control, comparing its aerodynamic performance and behavior against a conventional straight blade. Characterized by its ability to twist around its longitudinal axis under bending loads, the flexible curved blade is engineered to self-regulate in response to varying wind speeds, optimizing power output and enhancing operational safety. This design utilizes inherent elasticity and specific geometric configurations to develop torsional loads, resulting in continuous adjustment of the blade’s pitch angle via twist–bend deformation. The study focuses on a comparative analysis conducted in a wind tunnel, testing both a small-scale model of the conventional blade and the flexible curved blade of equivalent diameter. Results indicate that the flexible curved blade concept successfully moderates its rotational speed and power output at higher wind speeds and demonstrates the capability to start generating power at lower wind speeds and stabilize power effectively, aligning with sustainability goals by potentially reducing reliance on active control systems. Despite promising outcomes, passive control mechanisms did not activate at the designed wind speeds, revealing a misalignment between expected and actual performance and underscoring the need for further refinements in blade design and control settings. Additionally, the power coefficient (Cp) versus tip speed ratio (TSR) comparison showed that flexible curved blades operate within a lower TSR range and exhibit controlled capping of power under high wind conditions, marked by a distinctive ‘hook-like’ feature in Cp behavior. This study confirms the feasibility of designing and manufacturing passively controlled wind turbine blades tailored to specific performance criteria and underscores the potential of such technology. Future work, to be detailed in a subsequent paper, will explore further optimizations and the use of Glass Fiber-Reinforced Polymer (GFPR) composite materials to enhance blade flexibility and performance.

Adaptive inertia control strategy of asynchronous sending system based on coordination between DFIG and HVDC frequency limit controller

In a highly proportional hydropower asynchronous sending grid, the system is in a low damping state at all times because of the dual influence of the highly proportional hydropower water hammer effect and highly proportional DC outgoing rigid load, making frequency stability control difficult. In this study, an adaptive inertia control strategy for an asynchronous sending system based on the coordination of the wind turbine and high-voltage direct current (HVDC) frequency limiter controller (FLC) is proposed for a highly proportional hydropower asynchronous sending grid with a double-fed induction generator as the research object. The wind turbine and HVDC system provide virtual inertia support inside and outside the dead zone of FLC, respectively, and the additional virtual inertia-controlled FLC provides a fast response to the instantaneous power dip caused by wind turbine speed recovery. In addition, fuzzy control is introduced in the inertia control link of the wind turbine and FLC to dynamically adjust the virtual inertia adjustment coefficients of the wind turbine and HVDC system in accordance with the system frequency state to further enhance the coordination of frequency control. The effectiveness of the strategy is verified in a three-machine, single-return, DC outgoing system model.

Multidimensional hybrid software-in-the-loop modeling approach for experimental analysis of a floating offshore wind turbine in wave tank experiments

A hybrid experimental modeling approach for floating offshore wind turbines (FOWTs) in a wave tank is presented. The method called real-time hybrid method (RTHM) or software-in-the-loop (SIL) includes physically reproduced hydrodynamic loads in the wave tank, with a Froude-scaled model while aerodynamic loads are reproduced on the model by an actuator. The force is applied on the tower top and calculated by a numerical simulation running in real-time as a function of the measured velocity and position of the physical model. Couplings between the platform motion, the aerodynamic loads and the wind turbine controller are hence accounted for. A new SIL actuator enabling the reproduction of a multi-component force at the tower top of an experimental scaled wind turbine model is developed, and the quantification of the accuracy of the replicated forces is studied. The test case includes a floating spar platform carrying a 10 MW horizontal axis wind turbine. The results show that this SIL actuator can accurately replicate the low and wave-frequency aerodynamic loads, which are essential for wave tank testing. Lastly, a case study of the wind turbine model in colinear and misaligned wind and wave conditions is presented, showing 3D forces and induced motions.

Enhanced Modeling of Joint Yaw and Axial Induction Control Using Blade Element Momentum Methods

Wind turbine control via concurrent yaw misalignment and axial induction control has demonstrated potential for improving wind farm power output and mitigating structural loads. However, the complex aerodynamic interplay between these two effects requires deeper investigation. This study presents a modified blade element momentum (BEM) model that matches rotor-averaged quantities to an actuator disk model of yawed rotor induction, enabling analysis of joint yaw-induction control using realistic turbine control inputs. The BEM approach reveals that common torque control strategies such as K − Ω2 exhibit sub-optimal performance under yawed conditions. Notably, the power-yaw and thrust-yaw sensitivities vary significantly depending on the chosen control strategy, contrary to common modeling assumptions. In the context of wind farm control, employing induction control which minimizes the thrust coefficient proves most effective at reducing wake strength for a given power output across all yaw angles. Results indicate that while yaw control deflects wakes effectively, induction control more directly influences wake velocity magnitude, underscoring their complementary effects. This study advances a fundamental understanding of turbine aerodynamic responses in yawed operation and sets the stage for modeling joint yaw and induction control in wind farms.

Improvements to a novel learning algorithm for model-based wind turbine controllers

Wind turbine controllers are nowadays ever more advanced and rely on accurate internal controller model information. Therefore a calibrated model is needed for attaining predictable controller performance and ensuring stable operation. To calibrate the internal model information, a novel learning control scheme has recently been proposed that exploits the dynamics of the closed-loop controlled wind turbine system, without the need for wind speed measurements. The learning algorithm thereby periodically excites the generator power controller input signal. An extremum-seeking demodulation scheme was used to calibrate the internal model information. This paper improves the existing learning scheme in two ways: Firstly, it investigates how the frequency of the excitation signal influences the signal-to-noise ratio. Secondly, the problem was reformulated as a root-finding problem. This requires using the in-phase component of the phase-corrected learning signal. In addition, a precalculated lookup table relates the measured in-phase component directly to model uncertainty. It was found that an increased excitation frequency improves the signal-to-noise ratio (SNR) by an order of magnitude. Combined, these contributions improve the convergence speed more than twenty times, addressing the effect of aerodynamic degradation and its consequences on controller performance.

Two-Stage Coordinated Control of Type-4 Wind Turbine With Grid-Forming Ability for Active Damping Support

Two-Stage Coordinated Control of Type-4 Wind Turbine With Grid-Forming Ability for Active Damping Support

Optimal design of pendulum-tuned mass damper for the vibration control of offshore wind turbines with a flexible monopile foundation

Optimal design of pendulum-tuned mass damper for the vibration control of offshore wind turbines with a flexible monopile foundation

Dynamic interaction of inflow and rotor time scales and impact on single turbine wake recovery

The entrainment and recovery of wind turbine wakes are highly dependent on atmospheric inflow conditions, which has typically been quantified through the turbulent intensity. However, recent studies have shown that the integral time scales of the inflow has significant impact on the wake recovery. Concurrently, increased power production can also be achieved through intentionally introducing beneficial time scales by altering the control of the individual wind turbines. This study studies the combined impact of the dynamic interaction between dominant inflow and rotor time scales. The results show increased power production of a downstream wind turbine of more than 50% for the largest thrust coefficients and tip-speed ratios (TSR). However, the peak power gain occurs at different downstream positions indicating that combinations of inflow time scales and TSR = 6 result in faster near wake breakdown compared to the same inflow time scales combined with higher thrust coefficient of TSR = 8.

Dynamic wake conditions tailored by an active grid in the wind tunnel

Well characterized test environments are required for novel wind turbine and wind farm control concepts. Aeroelastic simulations are mostly used to model turbine aerodynamic and structural response. For wind farm control, also the wake behaviour needs to be represented, including the impacts of dynamic wind direction changes, wake meandering and the interaction of wakes with the atmospheric boundary layer. This paper shows how wake-like inflow conditions can be emulated with an active grid in a wind tunnel, exciting a broad band of turbulent scales. The artificial wake conditions can be used as inflow for an exposed model turbine. A focus is put on the meandering dynamics, which are driven by large transversal turbulence patterns in the atmospheric boundary layer. Following the conjecture of the Dynamic Wake Meandering (DWM) model, such turbulent scales must have the size of multiple rotor diameters, to impact the entire wake deficit like a passive tracer. In conventional wind tunnel experiments, such spatial scale ratios are hard to reach, since wind tunnel sizes are bounded while the model turbines must be sufficiently large to have appropriate aerodynamic scaling and instrumentation. In this work, quasi-stochastic meandering trajectories are created, using scaled Ornstein-Uhlenbeck processes. Thus, the intrinsically stochastic process of wake meandering is made repeatable. The paper focuses at a thorough characterization of the inflow conditions with both lidar and hot-wire measurements, considering wake shape and spectral features. The results show an approximately axi-symmetric Gaussian deficit, which meanders as a coherent structure while having spectral features similar to a turbine wake.

Norm-Optimal Iterative Learning Control for Wind Turbines During Grid Faults

Due to the increasing share of (offshore) wind turbines, more stringent requirements on power quality have been established. Importantly, the low-voltage ride-through grid requirement states that a wind turbine must remain connected to the electrical grid after a short intermittent grid fault. In the industry mainly gain-scheduled PID-controllers are used to mitigate the effects of grid faults on turbine operation, whereas more advanced solutions have been proposed in the literature such as model predictive control or multiple parallel PI-controllers. Remarkably, all controller implementations mentioned earlier are based on feedback control, where no feedforward strategies have been discussed in the literature. However, feedforward control could improve grid fault recovery performance by exploiting the relatively known fault characteristics by virtue of the specification in the Transmission System Operator requirements. Therefore, for the first time, a norm-optimal Iterative Learning Control (NO-ILC) algorithm is presented that solves these issues by learning the feedforward signal that optimally mitigates the effects of a grid fault. The NO-ILC algorithm applies model-free learning based on iterations, in which the framework of NO-ILC has been extended to include explicit input constraints. The goal of the NO-ILC is to reduce a (quadratic) cost function on specific input and output channels whilst conforming to specific input constraints by solving an optimisation problem, with, for this study blade pitch and rotor speed as respective input and output channels. It is shown that the NO-ILC algorithm can yield improved performance on a high-fidelity model, with a 45% decrease in the cost function used by NO-ILC compared to the nominal feedback control. The optimised feedforward signals resulting from NO-ILC can be used as an analysis tool to closer match the nominal grid fault feedback controllers response with that of NO-ILC, or directly applied as a library that can supplement the feedback controllers output during a grid fault.

Vibration Control of Wind Turbines through a Novel Reduced Column Section Approach: Numerical Application to the Onshore Wind Turbine in Sassoferrato, Italy

Vibration Control of Wind Turbines through a Novel Reduced Column Section Approach: Numerical Application to the Onshore Wind Turbine in Sassoferrato, Italy

Performance similarities between standard and retrofit LiDAR-assisted control for wind turbines

LiDAR-assisted control has proven to be a highly effective method for mitigating rotor speed deviations and reducing loads in wind turbines. This effectiveness stems from the ability of feedforward controllers to utilize incoming wind speed information obtained from LiDARs, enabling advanced blade pitch actions before the wind disturbs the turbine. However, the standard implementation of LiDAR-assisted control often necessitates modifications to the existing feedback controller, where the feedforward pitch rate is typically integrated into the feedback controller. This process can be challenging in terms of accessibility and may be limited to specific stakeholders, such as turbine manufacturers.A retrofit design provides an ideal solution, where the retrofit LiDAR-assisted controller modifies the rotor speed measurement to induce pitch actions without requiring alterations to the existing feedback controller. This paper aims to demonstrate the performance similarities between the standard LiDAR-assisted controller and its retrofit counterpart. Specifically, we establish that the retrofit LiDAR-assisted controller, with appropriate tuning, is equivalent to its standard counterpart. This equivalence implies that architecturally dissimilar controllers can yield the same performance in terms of rotor speed deviations and tower load reductions. The presented findings are supported by results obtained from high-fidelity closed-loop turbine simulations.

A Virtual Actuator for Advanced Individual Pitch Control (IPC)

We present a virtual actuator concept for wind turbine control, wherein rotor force and moment references from various controllers are optimally combined to compute individual blade pitch angles. The approach aims to minimize pitch bearing wear, too. The combined references can come from known control algorithms such as speed regulation, tilt-yaw control, tower dampening and helical wake control [3]. We formulate this as an optimization problem which we solve in an MPC fashion, however, instead of the usual prediction horizon over time, we use a discretized azimuth map as our finite horizon. Serving as a unified interface for all control features utilizing individual pitching, the virtual actuator replaces the plethora of multi-blade transformations and gain-scheduling functions in traditional IPC with one coherent function. Users can directly prioritize features and input constraints on actuators and structural loads. Notably, upstream control algorithms provide rotor force or moment references rather than pitch references. Simulations showcase the virtual actuator’s ability to compute intricate pitch trajectories, surpassing the capabilities of conventional IPC methods. Our method yields novel individual pitching which optimally merges conflicting IPC objectives while minimizing actuator wear.

Comparison of 25 MW downwind and upwind turbine designs with individual pitch control

As conventional upwind wind turbines grow larger, the increased mass and flexibility of the longer blades present challenges concerning costs, structural loads, and safety constraints such as tower clearance. At extreme scales, wind turbines in a downwind configuration may provide a feasible alternative to address these challenges by allowing lightweight, flexible blades that can reduce capital costs and blade loads while maintaining safety margins. Downwind turbine blades suffer from increased fatigue loading due to the tower shadow effect. In this study, novel downwind, three-bladed wind turbine designs at 25 MW rating with lightweight, flexible blades are evaluated and compared in terms of power production and structural loading. To obtain a baseline performance, a standard collective blade pitch wind turbine controller is implemented for the two downwind and one upwind turbine designs. Individual pitch control is then added for the downwind turbines to reduce structural fatigue on the turbine blades. In summary, the two downwind turbine designs that differ in rotor pre-coning and shaft tilt angles using collective and individual pitch control are compared against a conventional upwind turbine with collective pitch control at the same scale under turbulent wind conditions.

Analysis and evaluation of two reference LiDAR-assisted control designs for wind turbines

LiDAR-assisted wind turbine control holds promise in reducing structural loads and enhancing rotor speed regulation. However, a research gap exists in the practicality and limitations of commercially available fixed-beam LiDARs for large turbines and evaluating commonly employed LiDAR-assisted feedforward approaches. This study addresses these gaps by examining the implications of utilizing fixed-beam LiDARs in two wind turbine sizes and two reference LiDAR-assisted control strategies. A comprehensive evaluation considers coherence variations, uncertainties related to inaccurate pitch angle mapping with the upcoming wind speed, and their combined impact on load reduction. Numerical simulations reveal that an excessively low cut-off frequency in the low-pass filter can compromise preview time compensation. This is problematic in larger turbines, where coherence with limited LiDAR beams is inferior compared to smaller wind turbines, which deteriorates the effectiveness of the LiDAR-assisted control. Among the reference LiDAR-assisted control methods, the evaluation indicates the Schlipf approach has greater load reduction independence, while Bossanyi’s approach, which uses measurement of current blade pitch, yields positive results with fine-tuned baseline controllers. However, allowing baseline controller-induced frequencies to propagate into the controller may increase system excitation at certain frequencies due to the use of the actual pitch angle for feedforward pitch rate calculation.

Control-oriented wind turbine load estimation based on local linear neuro-fuzzy models

To enhance energy production, the wind energy industry builds increasingly larger wind turbines (WT) in terms of hub height and rotor diameter. This enlargement results in higher structural loads, which emphasizes the importance of load-reducing WT control alongside the nominal control of power and rotational speed. Generally, it is not possible to directly measure these structural loads, so a controller needs to include their online estimation. Here we extend a baseline WT state estimator in the form of an Extended Kalman Filter (EKF) to include two unaccounted loads. These loads are a change of thrust in the main bearing and a yaw moment in the rotor hub. We incorporate these loads via data-driven local linear neuro-fuzzy models (LLNFM). These LLNFMs can represent nonlinear relationships while maintaining limited complexity. We use alaska/Wind to generate the underlying regression data and to perform the state estimation both in simulation. The regression data consists of nominal WT operation under turbulent conditions for different average wind speeds. We further superimpose the individual pitch angles to excite the WT. The evaluation is performed in a different scenario, using another turbulent wind field and a realistic noisy sensor configuration. While we can estimate the change of thrust in the one per revolution (1P) frequency range, we achieve more precise results for the yaw moment due to the incorporation of sensors with a strong correlation to it. The estimation of the change of thrust and the yaw moment appear to be sufficiently precise for load-reducing WT control in practically relevant frequency ranges.

IoT-Based Monitoring, Communication, and Control of Small Wind Turbines Using IoT Cloud Service

Addressing global climate change requires a shift to sustainable and renewable energy sources. Utilizing wind power via wind turbines presents a viable remedy. However, because wind patterns are unpredictable and these turbines are located in remote areas, effectively operating wind farms continues to be a difficulty. In this work, we present an integrated system that uses IOT Cloud Service to monitor, connect with, and manage small wind turbines utilizing IoT (Internet of Things) technology. We solve the issues with data collection and control those older turbines without supervisory control and data acquisition (SCADA) systems confront by fusing cloud-enabled IoT hubs with on-site sensing units. Our method guarantees effective control of wind energy devices, two-way communication, and real-time monitoring. With an emphasis on utilizing IoT cloud services, this paper offers a thorough examination of the application of Internet of Things (IoT) technology for the monitoring, communication, and control of tiny wind turbines. In decentralized and off-grid energy applications, small wind turbines are vital, and maximizing energy output and guaranteeing long-term sustainability requires optimizing their performance and maintenance. The scalability, flexibility, and real-time capabilities required to efficiently operate small wind energy projects are sometimes absent from traditional monitoring and control systems.

Novel Fuzzy Logic Controls to Enhance Dynamic Frequency Control and Pitch Angle Regulation in Variable-Speed Wind Turbines

This study introduced a novel control approach based on fuzzy logic control (FLC) to enhance the frequency regulation capacity of variable-speed wind turbines (VSWTs). The proposed method integrates FLC within droop and inertia control loops. Real-time measurements of the system frequency and the rate of change of frequency (ROCOF) serve as inputs to the FLC, enabling the method to improve the frequency response by VSWTs. In addition, the method employs FLC for pitch angle frequency control, optimizing reserve power for frequency regulation under varying wind speed levels. The innovative aspect of this study lies in the simultaneous application of FLC to pitch angle frequency control and droop/inertia control, leading to the enhanced frequency regulation capability of VSWTs and smoother operation across a range of wind speeds. Compared with traditional methods, the proposed approach provides a comprehensive and effective solution to the challenges associated with frequency regulation in VSWTs. Through simulations across different wind speed scenarios, the proposed control method demonstrated the best performance among various mature methods, highlighting the efficacy of the proposed method on the frequency regulation of VSWTs under different wind speeds. This study’s findings highlight the potential of the proposed FLC-based method to optimize frequency regulation and contribute to more reliable and efficient wind energy systems.

Observer-based single-phase robustness sliding mode controller for the pitch control of a variable speed wind turbine

Observer-based single-phase robustness sliding mode controller for the pitch control of a variable speed wind turbine