Wind Farm Control Research Articles

An Additional Resonance Damping Control for Grey-Box D-PMSG Wind Farm Integrated Weak Grid

An Additional Resonance Damping Control for Grey-Box D-PMSG Wind Farm Integrated Weak Grid

Two-Level Distributed Consensus Control of Multiple Wind Farms for Fast Frequency Support

Two-Level Distributed Consensus Control of Multiple Wind Farms for Fast Frequency Support

Optimized Auxiliary Frequency Control of Wind Farm Based on Piecewise Reduced-order Frequency Response Model

Optimized Auxiliary Frequency Control of Wind Farm Based on Piecewise Reduced-order Frequency Response Model

Transient Stability Preventive Control of Wind Farm Connected Power System Considering the Uncertainty

Transient Stability Preventive Control of Wind Farm Connected Power System Considering the Uncertainty

Data-Driven Approach for Wind Farm Control: Toward an Alternative to FLORIS

Data-Driven Approach for Wind Farm Control: Toward an Alternative to FLORIS

Intelligent ADRC‐based inertia control for offshore wind farm

AbstractThis paper proposes a virtual inertia control strategy for wind farms based on auto disturbance rejection controller with artificial bee colony (ABC) algorithm. First, based on the system frequency dynamic response equation, an active disturbance rejection controller is designed according to the non‐linear feedback control law and the extended state observer to improve the anti‐interference capability of the virtual inertia control system. Second, in order to solve the problem of difficulty in tuning the parameters of the active disturbance rejection control (ADRC), an ABC algorithm based on nectar collection behaviour was proposed to iteratively optimize the parameters of ADRC. Finally, the proposed control method is effectively verified based on Matlab/Simulink. The simulation results show that compared with traditional algorithms, the proposed ABC‐ADRC can effectively control the rotor speed to adjust the frequency of the power grid system, achieve power sharing and adaptive noise elimination, reduce the complexity of parameter settings, not only improve anti‐interference ability, but also enhance the robustness of the system.

Synthetic turbulence modelling for offshore wind farm engineering models using coherence aggregation

AbstractTurbulent wind fields are known to be a major driver for structural loads and power fluctuations on offshore wind turbines. At the single‐turbine scale, there exist well‐established design standards based on wind spectra and coherence functions calibrated from years of measurements, which are used to generate multiple 10‐min wind field realisations known as synthetic turbulence boxes, themselves used as input to turbine‐scale aero‐hydro‐servo elastic codes. These methods are however not directly applicable at farm scale. When analysing the dynamics of large offshore wind farms, measurements reveal the importance of large, low‐frequency turbulent vortices for power fluctuations and hence for wind farm control and grid integration. Also, farm‐scale wind fields are needed as input to farm‐scale aero‐servo‐elastic codes for the modelling of wake dynamics, affecting structural loads. These new concerns motivate an upgrade in the original turbine‐scale wind field representation: (1) spectral models need to be based on farm‐scale measurements, (2) the frozen‐turbulence assumption merging temporal and along‐wind coherence must be lifted, (3) simplifications are needed to reduce the number of degrees of freedom as the domain becomes excessively large. This paper suggests models and algorithms for aggregated farm‐wide corrrelated synthetic turbulence generation—lumping the wind field into space‐averaged quantities—adapted to the aero‐hydro‐servo elastic modelling of large offshore wind farms. Starting from the work of Sørensen et al. in the early 2000s for grid integration purposes, methods for structural load modelling (through wake meandering and high‐resolution wind field reconstruction) are introduced. Implementation and efficiency matters involving mathematical subtleties are then presented. Finally, numerical experiments are carried out to (1) verify the approach and implementation against a state‐of‐the‐art point‐based—as opposite to aggregated—synthetic turbulence generation code and (2) illustrate the benefit of turbulence aggregation for the modelling of large offshore wind farms.

Reinforcement Learning-Based Wind Farm Control: Toward Large Farm Applications via Automatic Grouping and Transfer Learning

The high system complexity and strong wake effects bring significant challenges to wind farm operations. Conventional wind farm control methods may lead to degraded power generation efficiency. A reinforcement learning (RL)-based approach is proposed in this paper to handle these issues, which can increase the long-term farm-level power generation subject to strong wake effects while without requiring analytical wind farm models. The proposed method is significantly distinct from existing RL-based wind farm control approaches, whose computational complexities usually increase heavily with the increase of total turbine numbers. In contrast, our method can greatly reduce training loads and enhance learning efficiency via two novel designs: (1) automatic grouping and (2) multi-agent-based transfer learning (MATL). Automatic Grouping can divide a large wind farm into small turbine groups by analyzing the aerodynamic interactions between turbines and utilizing some key principles from the graph theory. It enables the separated conduction of RL algorithms on small turbine groups, avoiding the complex training process and high computational costs of applying RL on the entire farm. Based on Automatic Grouping, MATL can further reduce the computational complexity by allowing agents (i.e. wind turbines) to inherit control policies under potential group changes. Case studies with a dynamical simulator show that the proposed method achieves clear power generation increases than the benchmark. It also dramatically reduces computational costs compared with typical RL-based wind farm control methods, paving the way for the application of RL in general wind farms.

A novel yaw wake model for wind farm control applications

A novel yaw wake model for wind farm control applications

Fatigue Load Minimization for a Position-Controlled Floating Offshore Wind Turbine

Floating offshore wind farm control via real-time turbine repositioning has a potential in significantly enhancing the wind farm efficiency. Although the wind farm power capture increase by moving platforms with aerodynamic force has been verified in a recent study, the investigation and mitigation of the fatigue damage caused by such aerodynamic force manipulated for turbine repositioning is still necessary. To respond to these needs, this paper presents fatigue load controller design for a semisubmersible floating offshore wind turbine, particularly when the turbine position is controlled by the nacelle yaw angle. At various turbine positions determined by nacelle yaw angles and average wind speeds, the designed controller manipulates three blade pitch angles individually and minimizes the fatigue load at the tower base. As the individual blade pitch controller, the linear quadratic regulator is optimized through surrogate optimization by simulating the turbine disturbed by various turbulent wind and irregular wave profiles, and then by searching for a minimum fatigue from these simulations. Fatigue load analysis with the optimized controller leads to the main contribution of this paper, that is, to demonstrate that turbine repositioning can be achieved while allowing for the inclusion of a fatigue reducing controller. In fact, when operating the FOWT with the position controller and fatigue load controller, the fatigue damage at the tower base is reduced by about 40% for different nacelle yaw angles. This result supports the feasibility of position-controlled wind turbines to optimize the wind farm efficiency, thereby drastically reducing the offshore wind energy cost.

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

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

Data-driven wake model parameter estimation to analyze effects of wake superposition

Low-fidelity wake models are used for wind farm design and control optimization. To generalize to a wind farm model, individually modeled wakes are commonly superimposed using approximate superposition models. Wake models parameterize atmospheric and wake turbulence, introducing unknown model parameters that historically are tuned with idealized simulation or experimental data and neglect uncertainty. We calibrate and estimate the uncertainty of the parameters in a Gaussian wake model using Markov chain Monte Carlo (MCMC) for various wake superposition methods. Posterior distributions of the uncertain parameters are generated using power production data from large eddy simulations and a utility-scale wake steering field experiment. The posteriors for the wake expansion coefficient are sensitive to the choice of superposition method, with relative differences in the means and standard deviations on the order of 100%. This sensitivity illustrates the role of superposition methods in wake modeling error. We compare these data-driven parameter estimates to estimates derived from a standard turbulence-intensity based model as a baseline. To assess predictive accuracy, we calibrate the data-driven parameter estimates with a training dataset for yaw-aligned operation. Using a Monte Carlo approach, we then generate predicted distributions of turbine power production and evaluate against a hold-out test dataset for yaw-misaligned operation. For the cases tested, the MCMC-calibrated parameters reduce the total error of the power predictions by roughly 50% compared to the deterministic empirical model predictions. An additional benefit of the data-driven parameter estimation is the quantification of uncertainty, which enables physically quantified confidence intervals of wake model predictions.

Collective large-scale wind farm multivariate power output control based on hierarchical communication multi-agent proximal policy optimization

Wind power is becoming an increasingly vital source of renewable energy worldwide. However, controlling power generation in wind farms faces significant challenges due to the inherent complexity of these systems. To address this issue and maximize power output, we propose a novel communication-based multi-agent deep reinforcement learning approach for large-scale wind farm control. We introduce a multivariate power model for wind farms to analyze the impact of wake effects on power output. This model considers controllable variables such as axial induction factor, yaw angle, and tilt angle. To coordinate the continuous controls in large-scale wind farms, we propose the hierarchical communication multi-agent proximal policy optimization (HCMAPPO) algorithm. The wind farm is divided into multiple wind turbine aggregators (WTAs), and neighboring WTAs can exchange information through hierarchical communication to optimize power output. Our simulation results demonstrate that the multivariate HCMAPPO approach significantly increases wind farm power output compared to traditional PID control, coordinated model-based predictive control, and the multi-agent deep deterministic policy gradient algorithm. The HCMAPPO algorithm can be trained using a thirteen-turbine wind farm environment and effectively applied to large-scale wind farms. Importantly, as the wind farm scale increases, there is no significant increase in wind turbine blade fatigue damage from wake control. This multivariate HCMAPPO control strategy enables the collective maximization of power output in large-scale wind farms.

A three‐dimensional analytical model based on Gaussian distribution for wind‐turbine wakes

AbstractA Gaussian‐shaped, three‐dimensional (3DG‐k) wake model is proposed considering the shear wind effect. The model defines the wake radius rw to be equal to twice the Gaussian standard deviation σ and establishes a link with the expansion coefficient k of the physical wake boundary to describe the wake boundary. Garrad Hassan (GH) wind tunnel test data, Shiren wind farm measurement data, Nibe wind farm measurement data, and Vestas V80‐2MW wind turbine computational data by large eddy simulation method are used to validate the accuracy of the 3DG‐k, Jensen, 3D Gaussian (3DG), and 3D cosine (3D‐C) models. Results show that the 3DG‐k model exhibits higher accuracy for the four cases compared to other models. Due to its simplicity and universality, the model can be used for layout optimization and wake control of wind farms.

A FAST.Farm and MATLAB/Simulink interface for wind farm control design

Increasing the efficiency of wind farms is important for speeding up the transition from fossil fuels to renewable energy sources. Current wind farm control relies on maximization of power generation of individual turbines. However, research has demonstrated that plant-wide wind farm control could optimize the performance of a wind farm. Wind farm simulation tools are crucial in designing, testing, and validating wind farm controls. FAST.Farm is a recently developed multi-physics engineering tool for modeling wind farm performance by solving the aero-hydro-servoelastic dynamics of each individual turbine. The capabilities of FAST.Farm for control design purposes can be extended through a co-simulation with MATLAB/Simulink. Therefore, a MATLAB/Simulink interface with FAST.Farm has been developed. The creation and operation of this interface are explained in this paper. This interface supports developing and testing advanced closed-loop control at the wind turbine and wind farm levels.

Multi-Agent Reinforcement Learning Control of a Hydrostatic Wind Turbine-Based Farm

This paper leverages multi-agent reinforcement learning (MARL) to develop an efficient control system for a wind farm comprising a new type of wind turbines with hydrostatic transmission. The primary motivation for hydrostatic wind turbines (HWT) is increased reliability, and reduced manufacturing, operating, and maintaining costs by removing troublesome components and reducing nacelle weight. Nevertheless, the high system complexity of HWT and the wake effect pose significant challenges for the control of HWT-based wind farms. We therefore propose a MARL algorithm named multi-agent policy optimization (MAPO), which allows agents (turbines) to gradually improve their control policies by repeatedly interacting with the environment to learn an optimal operation curve for wind farms. Simulation results based on a wind farm simulator, FAST.Farm, show that MAPO outperforms the greedy policy and a popular learning-based method, multi-agent deep deterministic policy gradient (MADDPG), in terms of power generation.

Optimization of Communication Network for Distributed Control of Wind Farm Equipped With Energy Storages

Consensus based distributed control schemes have been widely applied to different operation scenarios of wind farms (WFs). However, few studies concern the influences of WF communication network topology to the consensus control performances. This paper proposes an optimal design method of the WF communication network for the consensus based re/active power regulation control of the WF, in which each individual wind turbine (WT) is equipped with an energy storage (ES) unit. Firstly, the basic model of the WT equipped with ES is presented, and the consensus based control scheme of the power regulation is described. Secondly, the optimization model for the communication network is constructed, so as to speed up the convergence rate, improve the communication survivability and reduce the communication links in the control. Thirdly, this paper proposes a bi-level alternating direction method of multipliers (ADMM) based solving framework, which decomposes the optimization into sub-problems. Finally, the performances of the control using the designed optimal communication network are tested and compared with other networks in the control, and the results verify the superiority of the proposed method.

Wind-speed forecasting model based on DBN-Elman combined with improved PSO-HHT

Random and fluctuating wind speeds make it difficult to stabilize the wind-power output, which complicates the execution of wind-farm control systems and increases the response frequency. In this study, a novel prediction model for ultrashort-term wind-speed prediction in wind farms is developed by combining a deep belief network, the Elman neural network, and the Hilbert-Huang transform modified using an improved particle swarm optimization algorithm. The experimental results show that the prediction results of the proposed deep neural network is better than that of shallow neural networks. Although the complexity of the model is high, the accuracy of wind-speed prediction and stability are also high. The proposed model effectively improves the accuracy of ultrashort-term wind-speed forecasting in wind farms.

Hierarchical power control of a large-scale wind farm by using a data-driven optimization method.

With the participation in automatic generation control (AGC), a large-scale wind farm should distribute the real-time AGC signal to numerous wind turbines (WTs). This easily leads to an expensive computation for a high-quality dispatch scheme, especially considering the wake effect among WTs. To address this problem, a hierarchical power control (HPC) is constructed based on the geographical layout and electrical connection of all the WTs. Firstly, the real-time AGC signal of the whole wind farm is distributed to multiple decoupled groups in proportion of their regulation capacities. Secondly, the AGC signal of each group is distributed to multiple WTs via the data-driven surrogate-assisted optimization, which can dramatically reduce the computation time with a small number of time-consuming objective evaluations. Besides, a high-quality dispatch scheme can be acquired by the efficient local search based on the dynamic surrogate. The effectiveness of the proposed technique is thoroughly verified with different AGC signals under different wind speeds and directions.

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

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