Turbine Control Research Articles

Going beyond BEM with BEM: an insight into dynamic inflow effects on floating wind turbines

Abstract. Blade element momentum (BEM) theory is the backbone of many industry-standard wind turbine aerodynamic models. To be applied to a broader set of engineering problems, BEM models have been extended since their inception and now include several empirical corrections. These models have benefitted from decades of development and refinement and have been extensively used and validated, proving their adequacy in predicting aerodynamic forces of horizontal-axis wind turbine rotors in most scenarios. However, the analysis of floating offshore wind turbines (FOWTs) introduces new sets of challenges, especially if new-generation large and flexible machines are considered. In fact, due to the combined action of wind and waves and their interaction with the turbine structure and control system, these machines are subject to unsteady motion and thus unsteady inflow on the wind turbine's blades, which could put BEM models to the test. Consensus has not been reached on the accuracy limits of BEM in these conditions. This study contributes to the ongoing research on the topic by systematically comparing four different aerodynamic models, ranging from BEM to computational fluid dynamics, in an attempt to shed light on the unsteady aerodynamic phenomena that are at stake in FOWTs and whether BEM is able to model them appropriately. Simulations are performed on the UNAFLOW 1:75 scale rotor during imposed harmonic surge and pitch motion. Experimental results are available for these conditions and are used for baseline validation. The rotor is analyzed in both rated operating conditions and low wind speeds, where unsteady aerodynamic effects are expected to be more pronounced. Results show that BEM, despite its simplicity, can adequately model the aerodynamics of FOWTs in most conditions if augmented with a dynamic inflow model.

Active power control of wind turbine based on fuzzy pitch control

With the continuous increase in wind power penetration rate, wind turbine generators (referred to as WTGs or wind turbines) need to have the capability of active power control (APC) to respond to grid power commands. To reduce the burden of pitch control on wind turbines, existing studies have adopted a technological approach that uses variable-speed operation of the wind rotor instead of pitch control. Typically, pitch control is only initiated at the speed boundaries to limit variable-speed operation within the speed range. However, due to the influence of factors such as wind speed, rotor speed and control parameters, there is a noticeable saturation phenomenon in pitch control at the speed boundaries, which can affect the variable-speed process of the wind rotor and APC performance within the speed range. Therefore, this paper determines whether blade pitch adjustment needs to be performed in advance when the wind turbine operates within the speed range based on the rotor speed state. This avoids the pitch action at the speed boundaries and the associated pitch saturation problem. The results show that the proposed method in this paper reduces the frequency of the rotor speed reaching the speed boundaries by performing pitch actions within the speed range. This alleviates the influence of pitch saturation on the variable-speed process of the wind rotor, as well as the occurrence of overspeed and electromagnetic power drop, thereby improving the APC performance of the wind turbine.

Impact of meteorological data factors and material characterization method on the predictions of leading edge erosion of wind turbine blades

Leading edge erosion of wind turbine blades is a major contributor to wind farm energy yield losses and maintenance costs. Presented is a multidisciplinary framework for predicting rain erosion lifetimes of wind turbine blades. Key aim is assessing the sensitivity of lifetime predictions to: modeling aspects (material erosion model, blade aerodynamics), input data and/or their preprocessing (joint frequency distribution of wind speed and droplet size based on synchronous site-specific measurements versus frequency distribution generated with partly site-agnostic modeling standards, wind speed records of nacelle anemometer or extrapolated at hub height from met masts), and environmental conditions (UV weathering). The analyses consider a Northwest England onshore site where a utility-scale turbine is operational, focus on a reference 5 MW turbine assumed operational at the site, and use a typical leading edge coating material. It is found that the largest variations in erosion lifetime predictions are due to material erosion model (based on rain erosion test data or fundamental material properties) and wind and rain model (measurement-based joint wind speed and droplet size distribution or standard-based modeled distribution). The use of joint wind and rain distribution also enables identifying wind/rain states with highest erosion potential, knowledge paramount to deploying erosion-safe turbine control.

Optimized design of multiple tuned mass dampers for vibration control of offshore wind turbines

Offshore wind turbines (OWTs) are dynamically sensitive structures to low-frequency wind-wave loadings due to their low damping and high flexibility. This makes them vulnerable to unwanted vibrations in ocean environments. Therefore, there is a need to implement innovative vibration control devices to suppress undesired vibration and ensure their structural safety. A tuned mass damper (TMD) has been a promising solution to control excessive vibrations recently in OWTs due to higher efficiency and low installation cost. However, TMD performance in vibration mitigation of OWTs when placed at a nacelle is still challenging to investigate because the generator torque and pitch control may affect the dynamics of the overall system. In this paper, an optimal multiple TMD system is designed by placing TMDs at optimal locations along the tower, which are defined using the maximum amplitude of the displacements for the first three natural frequencies of the tower. In addition, extensive simulations are carried out with integrated OWT-TMD systems to find the optimal mass ratio values and quantity of the TMDs. The TMD system is also tuned for several scenarios, including operating, parked, and idling conditions under the combined wind-wave loadings. A numerical model of the 5 MW NREL OWT developed in OpenFAST is considered as the baseline in this study. The results show a root mean square (RMS) response reduction of 10.2% and 42% in fore-aft (FA) and side-side (SS) tower displacement with optimally designed multiple TMDs. Moreover, improved mitigation effects on tower base moment of RMS 43.6% and 20.8% are observed in orthogonal directions, respectively. The findings of this paper may have the potential for designing passive TMD systems for vibration reductions in OWT.

The primary frequency modulation control strategy based on fuzzy active disturbance rejection control for gas turbines

Gas turbines play a core role in clean energy supply and construction of comprehensive energy system, and the control performance of primary frequency modulation (PFM) of gas turbine has a great impact on the frequency control of the power grid. However, there are some control difficulties in the PFM control of gas turbines, such as the coupling effect of fuel control loop and speed control loop, slow tracking speed and large variation of operating conditions. To relieve the above difficulties, a control strategy based on the fuzzy-modified active disturbance rejection control (F-MADRC) proposed in this paper. Based on the analyses of the parameter stability region for active disturbance rejection control (ADRC), fuzzy tuning rules of parameters for F-MADRC are designed. Finally, the proposed F-MADRC is applied to the PFM system of MS6001B heavy-duty gas turbine. The simulation results show that the gas turbine unit with the proposed method can obtain the best control performance of the PFM with strong ability to deal with system uncertainties. The proposed method shows good engineering application potential.

Maximum Power Point Tracking control of a variable speed wind turbine via a T-S fuzzy model-based approach

This paper proposes a multi-objective H2/H ∞ maximum power tracking control of a variable speed wind turbine to minimize the H2 tracking error and ensure the H ∞ model reference-tracking performance, simultaneously. The optimal condition is obtained via a boost converter use, which adapts the load impedance to the wind turbine generator. Thus, based on the fuzzy T-S model, a multi-objective Maximum Power Point Tracking (MPPT) controller is developed, ensuring maximum power transfer, despite wind speed variation and system uncertainty. To specify the optimal trajectory to follow, a TS reference model is proposed taking as input the optimal rectified DC current. The conditions of stability and stabilization are expressed in terms of linear matrix inequality (LMI) for uncertain and disturbed T-S models leading to determining the controller gains. Finally, an example of MPP tracking applied to a Wind Energy Conversion System (WECS) illustrates the effectiveness of the proposed fuzzy control law.

Study on Dynamic Response Characteristics of Offshore Floating Wind Turbine Pitch System

Due to its special working conditions, offshore wind turbine will bear large direct and indirect loads under the combined action of air flow and wave flow. In this paper, a variable pitch system composed of variablepitch motorand variable pitch bearing is improved, and the characteristics of system's bending moment, torque, vibration and other physical quantities under the action of multiple physical loads are verified, and the mechanical response characteristics of floating wind turbine under the control of unified variable pitch and independent variable pitch are studied under the running conditions at sea. The results show that mechanical structure of uniform pitch is compared with that of independent pitch, the independent variable pitch structure can effectively reduce the mean oscillation value of wind turbine tower in the parallel direction of air flow by optimizing control strategy, and reduce the thrust at the hub of wind turbine and the bending moment at the root of tower, but increase the vibration frequency and fatigue load of offshore wind turbine tower along parallel direction of air flow. Reduce the fatigue life of equipment. The research results can be used as a reference to reduce the variable pitch control and vibration suppression of offshore wind turbines and improve the reliability of wind turbines.

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

Anomaly detection of wind turbine based on norm-linear-ConvNeXt-TCN

The supervisory control and data acquisition (SCADA) system of wind turbines continuously collects a large amount of monitoring data during their operation. These data contain abundant information about the operating status of the turbine components. Utilizing this information makes it feasible to provide early warnings and predict the health status of the wind turbine. However, due to the strong coupling between the various components of the wind turbine, the data exhibits complex spatiotemporal relationships, multiple state parameters, strong non-linearity, and noise interference, which brings great difficulty to anomaly detection of the wind turbine. This paper proposes a new method for detecting abnormal operating conditions of wind turbines, based on a cleverly designed multi-layer linear residual module and the improved temporal convolutional network (TCN) with a new norm-linear-ConvNeXt architecture (NLC-TCN). Initially, the NLC-TCN deep learning reconstruction model is trained with historical data of normal behavior to extract the spatiotemporal features of state parameters under normal operational conditions. Subsequently, the condition score of the unit is determined by calculating the average normalized root mean square error between the reconstructed data and actual data. The streaming peaks-over-threshold real-time calculation of the anomaly warning threshold, based on extreme value theory, is then used for preliminary fault monitoring. Moreover, by shielding the fault alarm for low wind speeds and implementing a continuous delay perception mechanism, issues related to wind speed fluctuations and internal and external interference are addressed, enabling early warning for faulty units. Finally, the effectiveness and reliability of the proposed method are validated through comparative experiments using actual offshore wind farm SCADA data. The performance of the proposed method surpasses that of other compared methods. Additionally, the results of the proposed method were evaluated using the uniform manifold approximation and projection dimensionality reduction technique and kernel density estimation.

Deep reinforcement learning for PMSG wind turbine control via twin delayed deep deterministic policy gradient (TD3)

AbstractThis article proposes the use of a deep reinforcement learning method—and precisely a variant of the deep deterministic policy gradient (DDPG) method known as twin delayed DDPG, or TD3—for maximum power point tracking in wind energy conversion systems that use permanent magnet synchronous generators (PMSGs). An overview of the TD3 algorithm is provided, together with a detailed description of its implementation and training for the considered application. Simulation results are provided, also including a comparison with a model‐based control method based on feedback linearization and linear‐quadratic regulation. The proposed TD3‐based controller achieves a satisfactory control performance and is more robust to PMSG parameter variations as compared to the presented model‐based method. To the best of the authors' knowledge, this article presents for the first time an approach for generating both speed and current control loops using DRL for wind energy conversion systems.

Vibration control of a monopile offshore wind turbines under recorded seismic waves

The dynamics and vibration control of offshore wind turbines (OWTs) under wind, wave, and earthquake loads is an important but understudied topic, especially for fully coupled seismic analysis considering pile foundation flexibility of OWTs. In this work, we study the effect of multiple passive tuned mass dampers (MTMD) on a monopile OWT by using the fully coupled seismic analysis method. A combined shutdown procedures and MTMD vibration control strategy is proposed for the OWT. First, a state-of-the-art aeroelastic tool, FAST, is extended with a seismic load calculation module, and the numerically modeled monopile OWT addresses the effect of pile-soil interaction (PSI) by establishing ElasPile module in the updated FAST. Then, the focus is placed on the normal operation and emergency shutdown status of OWTs under the combined earthquakes, wind and wave conditions, and structural and motion responses are analyzed under various combination conditions through time-domain simulations, the dynamic characteristics of the OWT during realistic seismic events are shown. Sequentially, based on the proposed MTMD, the vibration migitation effects are evident for the operation OWT and the OWT with shutdown strategies during the combined loading conditions. The reduction in the extreme responses can be as high as 10.53% and 55.02% for the bending moment at the mudline in different OWT conditions, respectively. Therefore, the combined shutdown procedures and MTMD vibration control strategy of OWT is an effective vibration reduction method. Additionally, it is found that PSI has a significant impact on the dynamic characteristics and vibration control effects of OWT structures under wind, wave, and earthquake loads, and the coupled spring boundary condition on the basis of developed ElasPile module is recommended for seismic control analysis of OWT using the combined shutdown procedures and MTMD vibration control strategy.

Recurrent neural network for pitch control of variable-speed wind turbine.

Wind is one of the most widely used renewable energy sources due to its cost-effectiveness, power requirements, operation, and performance. There are many challenges in wind turbines, such as wind fluctuation, pitch control, and generator speed control. When the wind speed exceeds its rated value, the pitch angle controller limits the generator output power to its rated value. In this research work, several soft computing techniques have been implemented for pitch control of variable-speed wind turbine. The data is collected for the National Renewable Energy Laboratory offshore 5 MW baseline wind turbine. Wind speed, tip speed ratio, and power coefficient are taken as inputs, and pitch angle as output. Machine learning and artificial intelligence-based techniques such as recurrent neural networks (RNNs), adaptive neuro-fuzzy inference system (ANFIS), multilayer perceptron feed-forward neural network (MLPFFNN), and fuzzy logic controller (FLC) are implemented on MATLAB, and their results are evaluated in terms of mean square error (MSE) and root mean square error (RMSE). The controllers have been implemented in MATLAB/Simulink to schedule the wind turbine blade pitch angle and keep the output power stable at the rated value. The experimental results show that RNN provided the best results for 15 neurons in hidden layers and 1000 epochs with MSE of 3.28e-11 and RMSE of 5.54e-06, followed by MLPFFNN with MSE of 2.17e-10 and RMSE of 1.56e-05, ANFIS with MSE of 8.5e-05 and RMSE of 9.22e-03, and FLC with MSE of 6.25e-04 and RMSE of 0.025. The proposed scheme is more reliable and robust and can be easily implemented on a physical setup by using interfacing cards such as dSPACE, NI cards, and data acquisition cards.

Multi-segment droop control and optimal parameter setting strategy of wind turbine for frequency regulation

Traditionally, virtual inertia is used as the control strategy for wind turbines when participating in frequency regulation. However, it has inherent defects in measurement error amplification due to the frequency differential. Also, when control delay of wind turbines is taken into consideration, virtual inertia is essentially a fast power response, just the same as droop control. Hence, considering the adaptability of droop control, it is valuable to explore a novel droop control method without virtual inertia, as the frequency response model of wind turbines. For this reason, a multi-segment droop control strategy is proposed to provide a reliable frequency regulation model for wind turbines that can be broadly applied to other inverter-based power sources. Firstly, based on the extended system frequency response model, a differential equation including piecewise time-varying coefficients is established, and the analytical expression of the frequency response is obtained using the impulse function balancing principle and the integration by parts algorithm. Subsequently, based on analytical expression, the performance of the presented strategy is theoretically analyzed via comparison with virtual inertia control, and the conclusion that the presented multi-segment droop control has better frequency regulation performance can be got. Furthermore, to achieve optimal frequency regulation performance, a Lagrangian function is established to determine the optimal parameters of the proposed control strategy. Finally, the performance of the proposed strategy was verified in a two-area system model constructed on MATLAB/Simulink. Results show that the proposed multi-segment droop control has better frequency regulation performance.

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.

Pitch angle control of wind turbines using model-free auto-tuned fractional order proportional derivative ATFOPD controller

The nonlinear dynamics of wind power plants, in addition to uncertainties such as unknown nonlinear disturbances and parameter variations make the design of a robust controller a difficult task. This paper proposes a robust fractional-order controller for the pitch angle control of a variable-speed wind turbine system. An auto-tuned fractional order proportional-derivative controller ATFOPD is designed taking the objectives of reducing pitch actuator fatigue, limiting output mechanical power and speed at their rated values above the rated wind speed. The phase, phase slope and modulus of the pitch actuator process at the fixed gain crossover frequency are needed in order to tune the fractional order controller parameters. These values are usually derived using a mathematical model of the system, such as a transfer function. A model-free auto-tuning technique that can calculate the controller's parameters from measured variables is used in this work in order to design a robust FOPD controller. Simulation results of the ATFOPD controlled system are compared to integer order proportional-derivative IOPD and IOPID controllers, which show that the ATFOPD controller is more effective than the other controllers and its robustness is verified in the presence of disturbances in addition to system parameters and wind speed variations.

A GCN-based adaptive generative adversarial network model for short-term wind speed scenario prediction

Wind prediction is of great significance for wind energy utilization due to the stochastic nature of wind. To effectively facilitate various downstream decision-making tasks such as wind turbine control, predictive wind Scenario Generation (SG), which is capable of providing a set of deterministic instantiated wind prediction results, plays a critical role. In this paper, a novel Graph neural networks-based Adaptive Predictive Generative Adversarial Network (GAPGAN) model is proposed for accurate prediction of short-term future scenarios of a wind field. In GAPGAN, the original multivariate time series data are first reconstructed into the form of a graph, and spatiotemporal features are then extracted using Graph Convolutional Networks (GCNs). Next, a predictive generative adversarial network (PGAN) framework is proposed, which could generate different outputs corresponding to given historical observations as conditions. Finally, an adaptive PGAN training mechanism is introduced to stabilize the training process, and the best SG model is selected based on the proposed comprehensive evaluation system. Based on wind speed data collected from 11 wind turbines, computational experiments validate that the GAPGAN outperforms five benchmarking models in terms of point prediction accuracy, shape similarity, uncertainty prediction quality, and prediction scenario diversity.

Dynamic safety control of offshore wind turbine based on model predictive control

Offshore wind power is a clean energy source with excellent development prospects that are conducive to realising China's goal of carbon neutrality. However, offshore wind turbines (WTs) are prone to failure and damage owing to their long-term operation in marine environments with high humidity, high wind speeds, and frequent extreme weather. To ensure the safe operation of offshore WTs, a modified dynamic model predictive control (MPC) method was proposed in this study. First, a dynamic model of an offshore WT was established and its nonlinear state-space equations were obtained. These state-space equations were subsequently linearised and discretised, then fast trajectory tracking of the state variables was performed according to the conventional MPC principle. Finally, a control strategy was designed to realise the safe operational control of offshore WT using the safety index as a constraint condition in the MPC. Simulation results confirmed that the proposed MPC strategy using the safety index-based constraint exhibited superior tracking of the set trajectory and achieved the minimum energy loss under varying wind speeds.

A comprehensive overview of wind turbine controller technology: Emerging trends and challenges

The exploitation of nature to convert energy to electrical power is the most important rule in power generation. Wind energy is one of the most important of those energies that are widely available, and its use does not affect the environment significantly compared to fossil energy. On the other hand, and in recent times, researchers have made great efforts in the field of intelligent control and optimization, which has led to great leaps in the development of these sciences. In this paper, a detailed study is proposed for filling the gaps and conducting an updating state-of-arts of the last pitch control methods in the wind turbine systems. The review is conducted by comparing the key requirements related to control, complexity, stability and speed rangeability. Furthermore, a new classification for the general controller is introduced according to the techniques. Several recommendations for future research related to the control and technical evaluation of wind energy are presented. In sum, the appropriate classification of such important issues and identification of their advantages and drawbacks may greatly contribute to find better solutions.

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

Analysis and Mitigation of Electromechanical Oscillations in Drivetrain for Hybrid Synchronization Control of DFIG-Based Wind Turbines

Analysis and Mitigation of Electromechanical Oscillations in Drivetrain for Hybrid Synchronization Control of DFIG-Based Wind Turbines