Search Results (11)

Wind energy is paramount to the European Union’s decarbonization and electrification goals. As wind farms expand with larger turbines and more powerful generators, conventional ‘greedy’ control strategies become insufficient. Coordinated control approaches are increasingly needed to optimize not only power output but also structural loads, supporting longer asset lifetimes and enhanced profitability. Despite recent progress, the effective implementation of multi-objective wind farm control strategies—especially those involving yaw-based wake steering—remains limited and fragmented. This study addresses this gap through a structured review of recent developments that consider both power maximization and fatigue load mitigation. Key concepts are introduced to support interdisciplinary understanding. A comparative analysis of recent studies is conducted, highlighting optimization strategies, modelling approaches, and fidelity levels. The review identifies a shift towards surrogate-based optimization frameworks that balance computational cost and physical realism. The reported benefits include power gains of up to 12.5% and blade root fatigue load reductions exceeding 30% under specific scenarios. However, challenges in model validation, generalizability, and real-world deployment remain. AI emerges as a key enabler in strategy optimization and fatigue damage prediction. The findings underscore the need for integrated approaches that combine physics-based models, AI techniques, and instrumentation to fully leverage the potential of wind farm control. Full article

Wind farm control methods allow for a more flexible use of wind power plants over the baseline operation. They can be used to increase the power generated, to track a reference power signal or to reduce structural loads on a farm-wide level. Model-based control strategies have the advantage that prior knowledge can be included, for instance by simulating the current flow field state into the near future to take adequate control actions. This state needs to describe the real system as accurately as possible. This paper discusses what state estimation methods are suitable for wind farm flow field estimation and how they can be applied to the dynamic engineering model FLORIDyn. In particular, we derive an Ensemble Kalman Filter framework which can identify heterogeneous and changing wind speeds and wind directions across a wind farm. It does so based on the power generated by the turbines and wind direction measurements at the turbine locations. Next to the states, this framework quantifies uncertainty for the resulting state estimates. We also highlight challenges that arise when ensemble methods are applied to particle-based flow field simulations. The development of a flow field estimation framework for dynamic low-fidelity wind farm models is an essential step toward real-time dynamic model-based closed-loop wind farm control. Full article

The power system of wind farms is generally characterized by a weak grid, in which voltages may be heavily distorted and imbalanced, challenging the control scheme of wind-power converters that must be impervious to such disturbances. The control scheme in the stationary natural abc-frame has shown good performance under non-ideal voltage conditions, and then this paper proposes to analyze the operational performance of a wind-power system based on a permanent magnet synchronous generator subject to non-ideal conditions of the grid voltage, with its control scheme devised in the abc-reference frame. The proposed control scheme considers the torque control decoupling the flux and torque for the generator-side, showing the possibility to implement the machine torque control, without any coordinates transformation using a closed loop dot-product approach, between the field flux and stator currents. For the grid-side converter, the load current compensation is proposed, using the load current decomposition and conservative power theory (CPT), improving the grid power quality. The simulation results estimate the performance of the grid-side control under distorted and asymmetrical voltages, and the generator-side control against torque disturbances due to wind speed variation. Finally, experimental results in a small-scale test bench validate the proposed control scheme in injecting active and reactive power into the grid, and the torque control under wind speed variation. Full article

As the proportion of large-scale wind farms and distributed wind power connected to the power grid increases annually, the effects of their intermittent and random characteristics on the active and reactive power fluctuations of the power grid are becoming increasingly evident, causing frequent voltage fluctuations at the grid-connected point. To solve these problems, this study proposes a new topology of an electromagnetic var compensator (EVC) based on a rotary phase-shifting transformer (RPST). The EVC can work under capacitive and inductive conditions to compensate for inductive and capacitive power, respectively. In accordance with the parallel steady-state mathematical model of the EVC, a double closed-loop control strategy with high precision and considerable robustness is proposed for the EVC on the basis of instantaneous reactive power theory. Finally, simulations show that the topology of the proposed EVC exhibits bidirectional and continuous adjustment capability that can meet the reactive power compensation requirements of power systems with a high percentage of wind power. Compared with the existing reactive power compensation device, the EVC exhibits the advantages of high voltage, large capacity, low cost, strong impact resistance, and good tolerance, imbuing it with great prospects for development. Full article

In most DC power systems, power electronic devices can introduce ripple content into the DC grid, where large input ripple currents on the DC link can have a negative influence. Under these circumstances, DC side filters play an important role in the reduction of ripple content. In this paper, based on a full detailed closed loop model of the entire offshore wind farm multi-terminal DC network, the effect of filter capacitors connected at different sections of the system on the limitation of the AC ripple content, particularly in the DC cables, is studied. In contrast to other work on HVDC for offshore wind, where simplified or equivalent circuits are mainly used while concentrating on the power and control system, this study can be regarded as the specific study of filter capacitors operating within a detailed system model. Utilising the advantages of PLECS, which is a highly effective tool in the simulation of power electronic circuits, no small-signal or simplified equivalent models are used in the system, and the entire study is based on detailed and accurate models of the semiconductor elements and transformers, which help to provide more realistic simulation results and a better understanding of the system. Another novel point in this paper is a new concept, relative losses, which is proposed to simplify extensively the calculation of losses in this research. Finally, the size of the filter capacitors at different sections of the system under different situations is suggested. Full article

Subsynchronous oscillation, caused by the interaction between the rotor side converter (RSC) control of the doubly fed induction generator (DFIG) and series-compensated transmission line, is an alleged subsynchronous control interaction (SSCI). SSCI can cause DFIGs to go offline and crowbar circuit breakdown, and then deteriorate power system stability. This paper proposes a novel adaptive super-twisting sliding mode SSCI mitigation method for series-compensated DFIG-based wind power systems. Rotor currents were constrained to track the reference values which are determined by maximum power point tracking (MPPT) and reactive power demand. Super-twisting control laws were designed to generate RSC control signals. True adaptive and non-overestimated control gains were conceived with the aid of barrier function, without need of upper bound of uncertainty derivatives. Stability proof of the studied closed-loop power system was demonstrated in detail with the help of the Lyapunov method. Time-domain simulation for 100 MW aggregated DFIG wind farm was executed on MATLAB/Simulink platform. Some comparative simulation results with conventional PI control, partial feedback linearization control, and first-order sliding mode were also obtained, which verify the validity, robustness, and superiority of the proposed control strategy. Full article

Methods for wind farm power optimization through the use of wake steering often rely on engineering wake models due to the computational complexity associated with resolving wind farm dynamics numerically. Within the transient, turbulent atmospheric boundary layer, closed-loop control is required to dynamically adjust to evolving wind conditions, wherein the optimal wake model parameters are estimated as a function of time in a hybrid physics- and data-driven approach using supervisory control and data acquisition (SCADA) data. Analytic wake models rely on wake velocity deficit superposition methods to generalize the individual wake deficit to collective wind farm flow. In this study, the impact of the wake model superposition methodologies on closed-loop control are tested in large eddy simulations of the conventionally neutral atmospheric boundary layer with full Coriolis effects. A model for the non-vanishing lateral velocity trailing a yaw misaligned turbine, termed secondary steering, is also presented, validated, and tested in the closed-loop control framework. Modified linear and momentum conserving wake superposition methodologies increase the power production in closed-loop wake steering control statistically significantly more than linear superposition. While the secondary steering model increases the power production and reduces the predictive error associated with the wake model, the impact is not statistically significant. Modified linear and momentum conserving superposition using the proposed secondary steering model increase a six turbine array power production, compared to baseline control, in large eddy simulations by $7.5\%$ and $7.7\%$, respectively, with wake model predictive mean absolute errors of $0.03P_1$ and $0.04P_1$, respectively, where $P_1$ is the baseline power production of the leading turbine in the array. Ensemble Kalman filter parameter estimation significantly reduces the wake model predictive error for all wake deficit superposition and secondary steering cases compared to predefined model parameters. Full article

Wake models play a fundamental role in finding optimized solutions in wind farm control. In fact, they allow assessing how wakes develop and interact with each other with the agility required for real-time applications. In this paper, a Gaussian Wake Model (GWM) is implemented in the OpenFAST framework in a way such that its fidelity is increased with respect to previously implemented models, while enhancing its compatibility with control purposes. The OpenFAST tool is coupled with Floris, NREL’s software based on the GWM, in order to simulate the wake effect on downstream machines (in the case where the downstream rotor is fully covered by the wake, only partially covered by the wake, of the wake is generated by the interaction of more than one turbine), while the rotor aerodynamics is calculated using the BEMT on the actual rotor flow field. We intend this work as a starting point for developing and testing open/closed-loop control logics that will work in real wind farms. To show the suitability of the implementation, the entire model is then compared to Floris. Full article

In the last few years, one of the most important challenges of power technologies has been the integration of traditional energy production systems and distributed energy resources. Large-scale photovoltaic systems and wind farms may decrease the quality of the electrical grid service, mainly due to voltage and frequency peaks and fluctuations. Besides, new functionalities, such as the operation in islanded mode of some portions of the medium-voltage grid, are more and more required. In this respect, a model predictive control for voltage and frequency regulation in interconnected local distribution systems is presented. In the proposed model, each local system represents a collection of intelligent buildings and microgrids with a large capacity in active and reactive power regulation. The related model formalization includes a linear approximation of the power flow equations, based on stochastic variables related to the electrical load and to the production from renewable sources. A model predictive control problem is formalized, and a closed-loop linear control law has been obtained. In the results section, the proposed approach has been tested on the Institute of Electrical and Electronics Engineers(IEEE) 5 bus system, considering multiple loads and renewable sources variations on each local system. Full article

This study was conducted to analyze the impact of surrounding environmental changes on the feedback gain and performance of a closed-loop wind farm controller that reduces the error between total power output of wind farm and power command of transmission system operator. To analyze the impact of environment changes on wind farm controller feedback gain, the feedback gain was manually changed from 0 to 0.9 with a 0.1 interval. In this study, wind speed and wind direction changes were considered as environment changes; it was found by simulation code that the wind farm controller gain is in inverse proportion to wake recovery rate. In other words, the feedback gain should be higher if the distance between upstream and downstream wind turbine is not sufficient to wake recovery. Furthermore, the feedback gain should be lower when the upstream wind turbine generates a relatively weak wake by operating above the rated wind speed. The wind farm simulation was performed using reference 5 MW wind turbines from the National Renewable Energy Laboratory (NREL), which are numerically modeled for each element so that wind farm power output and tower load can be calculated according to the variation of the power command by using a modified wake model with improved accuracy. All the simulations performed in this study were carried out to review the power output accuracy of wind farms, but only if the transmission system operator’s power command was lower than the available power of wind farm. In this study, the gain of the wind farm controller was applied differently depending on the wind speed and direction to consider benefits in terms of power and tower load, especially if the wake effect of the upstream wind turbine was rapidly transferred to the downstream wind turbine. Ultimately, a simple, but more effective, power distribution method was proposed for distributing power commands to wind turbines that constitute wind farms and the study indicated the need for controller gain adjustment based on surrounding environmental changes. Full article

To support doubly fed wind turbine (DFWT) groups in offshore wind farms, this paper proposes a distributed coordinated control based on the Hamiltonian energy theory. This strategy provides global stability to closed-loop systems and facilitates output synchronization. First, a model of a DFWT is realized as a port-controlled Hamiltonian system with dissipation (PCH-D), and the single-machine model is expanded into a multi-machine model of a wind turbine group. Then, by using the design methodology of distributed Hamiltonian systems, a distributed coordinated control is presented for a multi-machine PCH-D system. Furthermore, to investigate failures in wind turbine groups, they are divided into two cases: the separation of failed machines from the system, and the grid-connected operation of failed machines after a fault. These cases correspond to undirected and directed graphs, respectively. Finally, simulations prove that distributed coordinated control enhances the reliability and autonomy of wind turbine groups in offshore wind farms. Full article