A quantitative review of wind farm control with the objective of wind farm power maximization

Thin film solid oxide fuel cells (TF-SOFCs) are one of the promising portable power sources in terms of rapid on/off, compact system volume, and high power density. This paper reviews fabrications, and designs of TF-SOFCs operating the temperature range of 300-500°C. The fabricating processes of both the electrolyte and the electrode via chemical vapor depositions and physical vapor depositions are discussed. The two platforms for designing TF-SOFCs are described: porous substrate and free-standing structures.

Thin Film Solid Oxide Fuel Cells Operating Below 600°C: A Review

Experimental observations of active blade pitch and generator control influence on floating wind turbine response

Wind farm control design is a recently new area of research that has rapidly become a key enabler for the development of large wind farm projects and their safe and efficient connection to the power grid. A comprehensive review of the intense research conducted in this area over the last 10 years is presented. Part I reviews control system concepts and structures and classifies them depending on their main objective (i.e. to maximise power production or to provide grid services. The work and key findings in each paper are discussed in detail with particular emphasis on the turbine side. Additionally, the review contributes to the existing reviews on the area by providing an elegant classification between model testing and control approaches. Areas where significant work is still needed are also discussed. In Part II, a thorough review on aerodynamic wind farm models for control design purposes is provided.

Wind farm control - Part I: A review on control system concepts and structures

. Engineering wake models provide the invaluable advantage to predict wind turbine wakes, power capture, and, in turn, annual energy production for an entire wind farm with very low computational costs compared to higher-fidelity numerical tools. However, wake and power predictions obtained with engineering wake models can be insufficiently accurate for wind farm optimization problems due to the ad hoc tuning of the model parameters, which are typically strongly dependent on the characteristics of the site and power plant under investigation. In this paper, lidar measurements collected for individual turbine wakes evolving over a flat terrain are leveraged to perform optimal tuning of the parameters of four widely used engineering wake models. The average wake velocity fields, used as a reference for the optimization problem, are obtained through a cluster analysis of lidar measurements performed under a broad range of turbine operative conditions, namely rotor thrust coefficients, and incoming wind characteristics, namely turbulence intensity at hub height. The sensitivity analysis of the optimally tuned model parameters and the respective physical interpretation are presented. The performance of the optimally tuned engineering wake models is discussed, while the results suggest that the optimally tuned Bastankhah and Ainslie wake models provide very good predictions of wind turbine wakes. Specifically, the Bastankhah wake model should be tuned only for the far-wake region, namely where the wake velocity field can be well approximated with a Gaussian profile in the radial direction. In contrast, the Ainslie model provides the advantage of using as input an arbitrary near-wake velocity profile, which can be obtained through other wake models, higher-fidelity tools, or experimental data. The good prediction capabilities of the Ainslie model indicate that the mixing-length model is a simple yet efficient turbulence closure to capture effects of incoming wind and wake-generated turbulence on the wake downstream evolution and predictions of turbine power yield.

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Optimal tuning of engineering wake models through LiDAR measurements

Simple demanded power point tracking (DPPT) control algorithms with a mode switch are proposed and experimentally validated in this study. One is a torque-based control and uses the generator torque with fixed blade-pitch angle. The other is blade-pitch based and uses both the generator torque and blade pitch for DPPT control. Both control algorithms receive power demand from a higherlevel controller and use their control strategies to track it. The two DPPT control algorithms were integrated with the basic torque and pitch control algorithms, and simulations using an in-house code and a high fidelity multi-body dynamic aeroelastic code were performed for steady and dynamic conditions. To verify the algorithms experimentally, wind tunnel tests with a scaled wind turbine having active pitch control capability were performed. From the simulation and the test, the proposed DPPT algorithms integrated into the conventional control algorithm were found to work well with the basic wind turbine power control algorithm. The torquebased control showed better performance in terms of power tracking, but the pitch-based control showed better performance in terms of loading.

Design and Validation of Demanded Power Point Tracking Control Algorithm of Wind Turbine

A strategy to increase the power output from a wind farm by regulating the most upstream wind turbine to a lower power than its available power is investigated in this study. A similar concept but using a fixed pitch angle of the most upstream wind turbine available in the literature was extended to use power demands from a wind farm controller, and the effect of wind turbine spacing and the wind speed on the power increase were analyzed. From various simulations of a virtual wind farm consisting of ten 5MW wind turbines in series using a time-domain wind farm simulation tool, the optimal power demand from the wind farm controller to increase the total power output from the wind farm was investigated. When the wind turbine spacing is 4D, the increase in the total power output from the wind farm reached 4.1% when the partialization is about 0.825. It was found from the study that the optimal power demand varies with the wind turbine spacing. It was also found that for the same wind turbine spacing, the optimal power demand is about the same if the wind speed is below the rated wind speed and in the max-CP region. If the wind speed becomes higher and it is in the transition region or above the rated wind speed, the effect of the partialization to increase the total power decreased substantially.

Power regulation of upstream wind turbines for power increase in a wind farm

A new control algorithm for a MW class spar-type floating offshore wind turbine has been developed to improve its performance and to reduce its mechanical load. The new blade pitch control having two PI control loops applied for different wind speed regions were designed. The bandwidth of the controller at near above-rated wind speed region was made lower to keep the vibration mode of the floating platform from being driven, and the bandwidth of the controller at far above-rated wind speed region was made higher to improve the wind turbine performance. Also, a feedback control loop using the angular acceleration information of the nacelle in the fore-aft direction was designed additionally to reduce the tower vibration. To perform modeling and simulation, a commercial multidynamics simulation program widely used for wind turbine design and certification, DNV-GL Bladed was used. The DNV-GL Bladed simulation results for the proposed new control algorithm showed that the output performance is improved and the tower load is reduced in various wind speeds above the rated wind speed.

Control algorithm of a floating wind turbine for reduction of tower loads and power fluctuation

A new wind turbine simulation tool for a time-domain dynamic simulation was developed in this study. The tool consists of models of aerodynamics, drive train, generator, actuators, and controllers for pitch and yaw controls. For the wind turbine model, the NREL 5MW reference wind turbine was used. Using measured data, the developed tool was applied to predict annual energy production from the wind turbine at four different sites in a complex terrain of Korea. The results were compared with those predicted by a commercial frequency-domain program widely used to predict the annual energy production from a wind turbine. Without a yaw control, the predictions from the proposed tool were close to those from the commercial wind farm design program. Also, from simulations with and without yaw controls, the differences in power predictions were quantified. The results of this study suggest that the power production from a wind turbine can be predicted by the proposed time-domain wind turbine simulation tool with a proper yaw algorithm which is not available in commercial frequency-domain programs. Further research is needed to experimentally validate the simulation results with yaw algorithm.

Time-Domain Dynamic Simulation of a Wind Turbine Including Yaw Motion for Power Prediction

A new wind farm control algorithm that adjusts the power output of the most upstream wind turbine in a wind farm for power increase and load reduction was developed in this study. The algorithm finds power commands to individual wind turbines to maximize the total power output from the wind farm when the power command from the transmission system operator is larger than the total available power from the wind farm. To validate this wind farm control algorithm, a relatively high fidelity wind farm simulation tool developed in the previous study was modified to include a wind farm controller which consists of a wind speed estimator, a power command calculator and a simplified wind farm model. In addition, the wind turbine controller in the simulation tool was modified to include a demanded power tracking algorithm. For a virtual wind farm with three 5 MW wind turbines aligned with the wind, simulations were performed with various ambient turbulent intensities, turbine spacing, and control frequencies. It was found from the dynamic simulation using turbulent winds that the proposed wind farm control algorithm can increase the power output and decrease the tower load of the most upstream wind turbine compared with the results with the conventional wind farm control.

Kwansu Kim

Papers

Design and Validation of Demanded Power Point Tracking Control Algorithm of Wind Turbine

Power regulation of upstream wind turbines for power increase in a wind farm

Control algorithm of a floating wind turbine for reduction of tower loads and power fluctuation

Time-Domain Dynamic Simulation of a Wind Turbine Including Yaw Motion for Power Prediction

Model Based Open-Loop Wind Farm Control Using Active Power for Power Increase and Load Reduction