Wind Farm Control Research Articles

Comparative Analysis of Wind Farm Simulators for Wind Farm Control

This paper conducts a comparative analysis of three wind farm simulators, examining the influence of wake on the local wind speed and power output for downstream turbines using experimental data. The study features experiments in three distinct scenarios, evaluating differences among the simulators by calculating the local wind speed and power for each. Each simulator employs a unique wake model, which substantially affects the local wind speed experienced by downstream turbines. Furthermore, the experiment involves adjusting parameter values for each simulator to assess their respective impacts on wind farm performance. The findings of this research are expected to play an important role in investigations related to power optimization and wake effects in the wind farm control.

Smart cooperative control scheme for large-scale wind farms based on a double-layer machine learning framework

In the real-time cooperative control of large-scale wind farms, the simultaneous achievement of accuracy and efficiency by the optimization framework plays an indispensable role. This paper presents a new double-layer machine learning (ML) framework comprising an Artificial Neural Networks (ANN) yawed wake model and Bayesian ML algorithm to strike a desirable compromise between accuracy and efficiency. Given the control on the iteration number with the scale-up of the wind farm, a novel row-based control scheme is further put forward to improve the optimization rate by reasonably reducing the optimization parameters. Moreover, parametric analysis has been performed considering the wind distribution and layout configuration to explore its applicability compared with the general independent one. The study shows that the novel framework performs favorably in an accurate and efficient power prediction and optimization of the wind farm. The row-based control scheme can further improve the convergence rate of the double-layer optimization framework remarkably at the expense of a slight decrease in optimal power production. The divergence of the wind distribution can dwindle the power gain of the wake steering strategy and weaken the superiority of the row-based cooperative control scheme. The row-based cooperative control scheme is more applicable to the aligned layout than the staggered layout, and this advantage is enhanced with the increase of wind farm scale.

A unified optimization control of wind farms considering wake effect for grid frequency support

This paper proposed a unified active power optimization control of wind farms under wake effect. It takes the sum of the kinetic energy variation and pitch angle variation as the optimization objective and used the particle swarm algorithm to achieve the optimization results. The main feature of the proposed method is that it unifies the kinetic energy optimization under a low wind speed area, the pitch angle and kinetic energy trade-off optimization under a medium wind speed area, and the pitch angle optimization under a high wind speed area. Combined with the de-loaded power constraint, it can flexibly reach various optimal operating states of the wind farm. The simulation results show that the proposed method optimizes the rotor speed and pitch angle in different wind speed areas, and releases kinetic energy and/or increases the output power of the wind farm to provide frequency support by switching the operating states.

End-to-end wind turbine wake modelling with deep graph representation learning

Wind turbine wake modelling is of crucial importance to accurate resource assessment, to layout optimisation, and to the operational control of wind farms. This work proposes a surrogate model for the representation of wind turbine wakes based on a state-of-the-art graph representation learning method termed a graph neural network. The proposed end-to-end deep learning model operates directly on unstructured meshes and has been validated against high-fidelity data, demonstrating its ability to rapidly make accurate 3D flow field predictions for various inlet conditions and turbine yaw angles. The specific graph neural network model employed here is shown to generalise well to unseen data and is less sensitive to over-smoothing compared to common graph neural networks. A case study based upon a real world wind farm further demonstrates the capability of the proposed approach to predict farm scale power generation. Moreover, the proposed graph neural network framework is flexible and highly generic and as formulated here can be applied to any steady state computational fluid dynamics simulations on unstructured meshes.

A Novel Power Injection Priority Optimization Strategy for Voltage Support Control of PMSG-based Wind Farm

This article presents a novel power injection priority optimization-based control strategy for a PMSG-based wind farm. The proposed strategy seeks to provide voltage support while mitigating the frequency excursion resulting from voltage dip during the fault. To achieve this objective, the conventional fixed reactive power injection priority plan is replaced by an adaptive power injection priority plan in the proposed method. The control problem is formulated as a multi-objective function optimization problem, where the power injection priority plan is calculated by minimizing the shortfall of WF active power and maximizing the reactive power injection of WF. During the fault period, the optimized result is directly applied to the outer control of machine-side converter and grid-side converter to regulate the active/reactive power generation. The simulation system model and proposed optimal model are built in EMTDC/PSCAD and MATLAB, respectively, to verify the effectiveness of the proposed strategy. Test results demonstrate that the proposed control strategy is applicable to mitigate frequency excursion while complying with the voltage support requirement during the fault.

Power train degradation modelling for multi-objective active power control of wind farms

With the coming installation of hundreds of GW of offshore wind power, penetration of the inherent power fluctuations into the electricity grid will become significant. Therefore, the use of wind farms as power reserve providers to support the regulation of the grid’s voltage and frequency through delivering a desired power is expected to increase. As a result, wind turbines will not be necessarily delivering the maximum available power anymore – known as curtailed or derated operation – and will have to be able to deal with time-variant power demand. For this purpose, power setpoints from the grid are dispatched at the farm-level and then tracked at the turbine-level under the constraint of available power in the wind (known as active power control). The idea of this work is taking advantage of the additional degree of freedom lying in the power dispatch between turbines when operating in curtailed conditions. As failure of power train system components is frequent, costly and predictable, we seek to introduce power train degradation into the farm control objectives. To this end, a data-driven model of drivetrain fatigue damage as function of wind conditions and derating factor adapted to the farm active power control objective function is developed based on the pre-analysis of single-turbine simulations and degradation calculations, where the increased turbulence intensity due to wind farm wake effect is also considered. The proposed analytical power train degradation model is computationally efficient, can reflect the fatigue damage of individual gears and bearings in the overall power train life function and in contrast with high-fidelity models can be easily adjusted for different drivetrain configurations. A case study on the TotalControl reference wind power plant is demonstrated.

Wind energy-harvesting technologies and recent research progresses in wind farm control models

In order to sustain the overall competitiveness of the wind power industry, unrelenting focus is required on working toward the advancement of enabling technologies and research studies that are associated with wind farm systems. First, wind farm technologies that include various turbine generator systems coupled with different power transmission configurations have enormous impact in determining the quality of wind power production. In addition, modern wind farms are expected to implement robust power control algorithms to meet more advanced requirements of electricity generation. Accordingly, this study explores the statuses of wind energy harvesting technologies and wind farm control strategies by discussing their recent and future impact on transforming the wind power industry. Doubly fed induction generator (DFIG)-based wind energy harvesting technology is well-matured and has exhibited an excellent track-record in past and recent experiences, but its capability of being further scalable for large-scale power production is limited as it is largely incompatible with high-voltage power transmission networks. On the other hand, permanent magnet synchronous generator (PMSG)-based technology is making significant advancements to attain the maximum possible efficiency level in greatly facilitating larger scale power generation, although the construction of bulky and costly power transmission systems is required. In this regard, future technological advances in the wind farm industry are expected to reasonably optimize the design and cost of high-voltage power transmission systems. Similarly, an increasing number of research studies are introducing a number of power optimization-based control models to create an ideal integration of the aforementioned wind farm technologies so as to ultimately enhance the reliability of electricity production by maintaining the systems’ safety. Yet, additional work is still expected to be undertaken in the future for a more extended evaluation of the performances of many different control models under a similar environment.

Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review

As grid-connected wind farms become more common in the modern power system, the question of how to maximize wind power generation while limiting downtime has been a common issue for researchers around the world. Due to the complexity of wind turbine systems and the difficulty to predict varying wind speeds, artificial intelligence (AI) and machine learning (ML) algorithms have become key components when developing controllers and control schemes. Although, in recent years, several review papers on these topics have been published, there are no comprehensive review papers that pertain to both AI and ML in wind turbine control systems available in the literature, especially with respect to the most recently published control techniques. To overcome the drawbacks of the existing literature, an in-depth overview of ML and AI in wind turbine systems is presented in this paper. This paper analyzes the following reviews: (i) why optimizing wind farm power generation is important; (ii) the challenges associated with designing an efficient control scheme for wind farms; (iii) a breakdown of the different types of AI and ML algorithms used in wind farm controllers and control schemes; (iv) AI and ML for wind speed prediction; (v) AI and ML for wind power prediction; (vi) AI and ML for mechanical component monitoring and fault detection; and (vii) AI and ML for electrical fault prevention and detection. This paper will offer researchers and engineers in the wind energy generation field a comprehensive review of the application of AI and ML in the control methodology of offshore and onshore wind farms so that more efficient and robust control schemes can be designed for future wind turbine controllers.

DecoWind: Development of low-noise and cost-effective wind farm control technology

DecoWind is a 3-year Danish research project whose goal is to devise advanced control strategies for wind turbines and farms for minimizing their acoustic impact. Noise propagation models (Nord2000 and WindStar) are verified through dedicated measurement campaigns onshore. Long-range offshore measurements are also conducted to understand these specific conditions. A Parabolic Equation method for noise propagation, which have mostly been restricted to academic use in the past, is integrated into an engineering context for wind farm control. This framework can be used to define a wind farm optimal control strategy. The energy production is maximized while limiting the noise impact at dwellings, depending on the considered site, by operating the turbines using their different noise operational modes. This framework is demonstrated through numerical test-cases. In addition, a public survey is conducted to assess the socio-acoustical impact of wind turbine noise, looking at several factors. The ultimate goal is to produce a set of recommendations regarding wind turbine noise regulations that would connect the new engineering design capabilities and the findings regarding public annoyance. The project is a collaboration between DTU, Siemens-Gamesa Renewable Energy, FORCE Technology, and EMD International. In this contribution, the main achievements of the project are summarized.

A Critical Review of Current and Future Options for Wind Farm Participation in Ancillary Service Provision

This paper presents a critical review, from a wind farm control perspective, of different methodologies in the open literature that enable wind farms to participate in ancillary service provision. Firstly, it considers the services currently provided in power systems with high levels of wind generation (specifically, Denmark, Ireland, and Great Britain), reviewing current regulatory frameworks and recommendations. Secondly, it reviews the ancillary service markets that wind farms do not currently participate in, considering the barriers to entry and discussing potential solutions using a proper control-enabled framework. Thirdly, it also considers the future perspective for wind farm participation in ancillary service provision, including a review of the body of published academic research on wind farm participation in ancillary service provision. Finally, this review concludes by suggesting where the gaps are in the academic literature, and subsequently suggests future work. Two examples are the disconnect between the mechanical and farm side approaches with power-system-based modelling, and how much wind farm modelling is very-low-fidelity-omittedkey aspects such as wake effects and component fatigue analysis.

New design of a wind farm frequency control considering output uncertainty and fatigue suppression

Frequency response capability is necessary for wind farms (WFs) in the power system with a high proportion of renewable energy. In frequency control, WFs need to meet the grid’s power demand and avoid obvious additional damage to wind turbines (WTs) due to frequency control. In terms of meeting the grid power demand, the actual power characteristics of WTs need to be accurately described. An improved Gaussian Mixture Model (GMM) is used to model the actual measured output of WTs. The wind farm control center can accordingly issue achievable effective power commands to the WTs in the frequency response. In terms of avoiding the structural fatigue increase of WTs, a two-degree-of-freedom control strategy is designed, which simultaneously controls the generator torque and pitch angle and achieves fatigue suppression through multi-objective model predictive control (MOMPC). The simulation results show that the proposed control system can improve the frequency response capability of the WF and reduce the fatigue load of WTs, which has obvious application prospects.

Wind Energy Handbook: Of value for education and reference [Book Review

This is an extensive book covering the theory of operation behind wind turbine technology. The first edition, published 20 years ago, has had two updates over 10-year intervals, reflecting the evolutions of design rules and principal innovations in the technology. Offshore wind was added to the second edition, and a section on wake effects and wind farm control is added in this third edition as well as updates across all chapters. The writers are a group of renowned senior professionals in wind power technology from the United Kingdom. The book provides comprehensive coverage of wind turbine technology. The focus is on wind turbine design. A vast established knowledge base is covered in the book, suitable for use by students (final year undergraduate or postgraduate) and people involved in the design, manufacture, or operation of wind turbines. Although the book is not focused on electrical engineering, it covers most areas of electrical design: generator design; controls; transformers; power plant internal grids; and grid connection and protection. The offshore wind section includes the specificities of submarine cables. Exposing electrical engineers to the entirety of wind turbine design might be a good idea—just reading the introduction of each section and glancing through the content gives a great overall picture of wind turbine technology.

Model Predictive Control Based Coordinated Voltage Control for Offshore Radial DC-connected Wind Farms

In this study, a coordinated voltage control strategy based on model predictive control (MPC) is proposed for offshore radial DC-connected wind farms. Two control modes are designed in this strategy. In the economic operation mode, the wind farm controller generates optimal active power references as well as bus voltage references of medium-voltage collector for DC-connected wind turbine (DCWT) systems and high-voltage DC/DC converters, where the goal is to minimize power losses inside the wind farm and ensure that voltages are within a feasible range, all while tracking the power references. In the voltage control mode, the main control objective for the wind farm controller is to minimize voltage deviations from the rated voltage. With the MPC, the control objective and operation constraints can be explicitly represented in the optimization problem while considering the dynamic response of the DCWT system. In addition, a sensitivity coefficient calculation method for radial DC-connected wind farms is developed to improve computational efficiency. Finally, DC-connected wind farms with 20 wind turbines are used to demonstrate the performance of the proposed strategy.

Platform Oscillation Reduction of a Floating Offshore Wind Turbine

This paper focuses on the design of an individual blade pitch controller to reduce the platform oscillation of a floating offshore wind turbine with repositioning capacity. Such reduction is helpful for captured power regulation in wind farm control via turbine repositioning. In this study, a baseline 5MW wind turbine on a semi-submersible platform with long mooring lines is used for demonstration purposes. The model for the controller design is obtained as a state equation using the linearization functionality of the wind turbine simulator OpenFAST, activating only platform pitch and platform yaw as degrees of freedom. Based on the model, a linear quadratic regulator (LQR) is designed for platform vibration suppression. During each revolution, the controller prioritizes producing restoring moments to address platform pitch oscillation as the blades approach the vertical centerline of the rotor plane. Conversely, as the blades rotate close to the horizontal centerline of the rotor plane, the controller shifts its focus towards generating restoring moments to counteract platform yaw fluctuations. It is demonstrated in OpenFAST that the designed LQR-based individual blade pitch controller effectively reduces platform surge, roll, pitch, and yaw oscillation, resulting in improved power regulation during turbine repositioning.

Phase Synchronization for Helix Enhanced Wake Mixing in Downstream Wind Turbines

Wind farm controllers such as the Helix approach have shown potential in increasing plant power production through wake mixing. The concept suggests that actuating the upstream turbines’ blade pitching with a specific periodic signal can induce a helix-shaped wake, thereby alleviating wind velocity deficit on downstream turbines. Wake mixing initiation by downstream turbines may also be shown advantageous for power production; however, little to no attention has been given to such an approach. Similar wake mixing is expected to be achievable at lower control costs if the downstream turbine can benefit from the periodic component already present in the wake of the upstream turbine. Such a hypothesis is studied in this work by designing a minimal control scheme where the wake acting on the downstream turbine is simulated by a periodic input disturbance. A Kalman filter is proposed for incoming input disturbance phase estimation using SCADA data. The reconstructed phase information allows synchronization of the downstream control action with the periodic input disturbance by means of a phase synchronization wake mixing controller. The periodic component was estimated with a minimal root-mean-square error and the resulting control action was in phase with the input disturbance and demonstrated satisfactory performance even with a small phase perturbation. Future work will include applications in a high-fidelity wind turbine model and wind tunnel studies.

Two-Stage Optimal Re/Active Power Control of Large-Scale Wind Farm to Mitigate Voltage Dip Induced Frequency Excursion

A two-stage optimal active and reactive power coordinated control strategy of large-scale wind farm (WF) to mitigate voltage dip induced frequency excursion (VDIFE) is proposed in this letter. Before the voltage fault occurs, the power reference look-up table of each wind turbine (WT) are calculated. When voltage fault occurs, the power reference values of each WT are optimized based on model predictive control. Distinguished from existing works, the proposed optimal power control can better mitigate VDIFE and balance the variation of rotor speed of each WT. Case studies on a doubly-fed-induction-generator based WF built in MATLAB/Simulink are carried out to demonstrate the effectiveness of the proposed control scheme.

Jensen-ANN: A Machine Learning adaptation of Jensen Wake Model

Wake models play an important role in wind farm layout optimization and control studies and it is, therefore, important to model wake effects in accurate and efficient ways. The power production from a wind farm is estimated using analytical models such as Jensen model in the wind industry, as they are simple and their computational cost is significantly less compared to the high-fidelity models involving Large Eddy Simulations. As these analytical models are used in an iterative setup like control and optimization of wind layouts, simulation cost involved in these models is extremely important. However, most of the analytical models assume linear expansion of wake while modeling wake effects, which makes it inaccurate. In this paper, the authors present a data driven approach under the framework of machine learning to impart the effect of nonlinear expansion of wake in the analytical models and thereby empowering them to be more accurate. Such a wake model was developed by integrating Artificial Neural Networks (ANNs) and Jensen model, where the expansion of wake is assumed as nonlinear and is modeled using ANNs inside the Jensen model setup, thus establishing the nonlinearity between inter-turbine distance and reduction in wind speed. To prove the efficacy of the proposed model, the results are compared with the predictions of analytical Jensen model, and it has been shown that the proposed model performs better than Jensen, demonstrating the importance of nonlinear expansion of wake and its effect in power calculation of a wind farm.

Synchrophasor Data Based Q-V Droop Control of Wind Farm Integrated Power Systems

With increasing scalability of grid-integrated wind farms (WFs), reactive power ancillary service through Q-V droop control of doubly-fed inducton generators (DFIGs) is one of the prime interests of power system operators. However, variable and uncertain availability of wind-resource at the wind turbines creates challenge against allocating reactive power reserve at the WFs for maintaining voltage stability of the grid. This issue can be resolved through online coordination among the WFs. This paper proposes a synchrophasor data based Q-V droop (SQVD) control technique for the WFs integrated to the grid through different point of common couplings (PCCs) in the power system transmission network. The data from the phasor measurement units (PMUs) at the WF-connected PCCs are used to obtain synchronized Q-V droop gain for each WF. Further, distributed coordination among DFIGs within each WF is performed to allocate optimum reactive power share at the converters. The total reserve of each WF is employed by their synchronized droop, in improving voltage stability at both the local PCC and neighboring PCCs. Performance of SQVD is verified at 418-bus practical Indian power system, in different dynamic situations of the grid, and found to be more effective as compared to the constant Q-V droop and variable Q-V droop control methods.

Coordinated control of wind farm and supercapacitor energy storage system for dynamic performance reinforcement of multi-area power systems

Coordinated control of wind farm and supercapacitor energy storage system for dynamic performance reinforcement of multi-area power systems

Heteroscedastic Bayesian Optimisation for Active Power Control of Wind Farms*

Active power control of wind farms remains an open challenge due to inherent noise in wind power that arises from uncertain wind speed measurements and plant/model mismatch. To leverage the heteroscedastic nature of the wind power noise, heteroscedastic Bayesian optimisation (BO) is used for active power control of wind farms. BO utilises closed-loop performance data to tune the parameters of a stochastic model predictive controller (SMPC) in a systematic and data-efficient manner. This, in turn, allows for enhancing the closed-loop performance of the controller intended to decrease the power tracking error. A case study with 9 turbines in a 3 × 3 wind farm shows that the heteroscedastic BO approach achieves a reduced closed-loop power tracking error in terms of root-mean-square by 8.89% compared to one that relies on nominal BO and a decrease by 64.99% compared to a nominal model predictive controller (MPC) whose performance is not tuned using closed-loop data and BO.