This paper presents the implementation of heterogeneous flow and wind direction into the three-dimensional dynamic Smart WindPark Laboratory Wind Flow Model for wind farm simulation and HIL-based emulation. It extends its capabilities from a dynamic wind speed inlet and homogeneous wind direction to a wind speed inlet vector to consider heterogeneous inflow as well as by adding a dynamic wind direction matrix to the whole wind field in order to replicate heterogeneous wind directions within the wind farm domain. Both features provide more configuration options and allow to better adapt to wind conditions at specific sites or to investigate wind turbine control, wake propagation and dynamic interactions during fast and inhomogeneous wind changes. Based on previous work, the model framework is addressed and the extensions are discussed in detail. Simulation results present the additional model capabilities.

The Smart WindPark Laboratory Wind Flow Model (SWiPLab-WFM) has been developed to simulate three-dimensional wind farm aerodynamics along with wind turbine emulators, i.e. physical test benches, consisting of mechanical drive trains, generators and power electronics in order to consider all relevant cross-couplings of a real wind farm in multiple physical domains. This paper deals with model design and modularization of the SWiPLab-WFM to meet the requirements of a dynamic wind flow simulation. The decentralized real-time simulator cluster architecture, which is capable of computing the model for 15 turbine emulators in total is introduced, along with its hardware specifications and I/O properties. Communication, data exchange and implementation aspects are discussed and results concerning computation efficiency are presented.

State-of-the-art energy storage systems utilize many battery cells in series and parallel connection to provide the required energy and power for the individual use case. In such applications, e.g. electric mobility or grid energy storages, monitoring and balancing systems for every individual cell are required. Cell voltage deviations are naturally intrinsic and are caused by different chemical, electrical and thermal properties of each cell as well as cyclic and calendric aging. This paper deals with a new active flying inductor balancing concept and its implementation. In contrast to other topologies, which often transfer energy through adjacent cells only, the proposed approach enables direct energy transfer between any two cells requiring only one inductor as intermediate storage. The circuit topology and consecutive addressing of cells also allows implementation of cell voltage monitoring simultaneously to the balancing process, making additional sensors unnecessary. A customized implementation of algorithms, such as energy-based or voltage-based balancing is possible. Simulations and experimental results validate the proposed approach and indicate high efficiency and balancing rate.

This paper presents first results and validation aspects of the implementation of far wake behavior into the Smart WindPark Laboratory Wind Flow Model in order to predict the impact of wind turbines on downwind wind farms through their wakes. Due to cluster formation, especially offshore, wind farms are located relatively close to each other, resulting in complex aerodynamic couplings which depend on wind direction, wind speed, ambient turbulence and atmospheric stability as well as wind turbine operation. This leads not only to decreased wind speeds behind wind farms and consequently reduced power conversion of waked wind farms, but also to increased fatigue loads, both lowering return on investment. Simulation results obtained with the modified simulation model are presented in the paper and compared to measurement data from literature.

Holistic wind farm simulations need to consider multiple physical domains and a variety of interactions, ranging from aerodynamics to grid interdependence. In this paper, the concept and implementation aspects of a real-time capable power system model is presented, which is integrated into the laboratory scale Smart WindPark Laboratory infrastructure SWiPLab, where all relevant cross-couplings in the scope of wind farm dynamics are considered in a hybrid PHIL-based approach. This allows for grid stability investigations as well as optimization of overall wind farm management in the scope of increasing renewable energy sources in the power system. Grid services such as secondary reserves or grid-forming wind turbine control can be developed, validated and improved.

This paper presents a review based on Axial Induction Control (AIC), Wake Redirection Control (WRC) and artificial intelligence to support operation and optimal control of a wind farm. In addition to that, a simulation is carried out using Sim Windfarm in Matlab/Simulink, to generate a wind farm model with 8 wind turbines based on the NREL 5MW reference turbine. The data generated from the model consists of 24 measured values of wind speed and total power production by the wind farm. The main objective of the simulations is to generate data that can be used to predict the power output of the wind farm. The power prediction is done using nonlinear auto-regressive network with exogenous inputs (NARX) neural network, in which the measured wind speed is used as input to the NARX network, while power production is used as a target. The NARX configuration is based on a two-layer feed-forward network, with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer. The NARX network performs a one step ahead prediction for 1.25 seconds with small error. The prediction result indicates a good performance as confirmed by the error autocorrelation plot, regression graph and the response plot generated by the network. This prediction can be used to improve WRC, AIC, or turbines control in the range of seconds.

An increasing amount of electrical energy worldwide is produced by renewable energy sources such as wind turbines. Modern wind turbines consist of multiple mechanical and electrical subsystems which influence each other during operation. In order to improve the wind turbine design, it is desirable to simulate its operating behavior under varying conditions using accurate simulation models. This paper aims to develop a simulation model for a doubly-fed induction generator wind turbine which considers the relevant characteristics of the many subsystems involved. In this model, the wind rotor blades, mechanical drive train, generator system, and the surrounding wind field are represented and modeled in sufficient complexity. In a multi-rate simulation, the model is verified using a machine startup scenario as well as a wind gust scenario. The simulation results show the interaction between the wind rotor, drive train, and generator system. During wind speed changes, the generated power fluctuates before reaching a new steady-state condition. Oscillations occurring in the drive train put stress on the mechanical parts of the wind turbine and influence the power, transmitted to the point of common coupling.

Nowadays Grid-Connected Voltage Source Converters (GCVSC) are used in a wide range of applications, such as renewable energy systems, power conditioning or high power factor rectifiers. Recommended practices and requirements demand low harmonic distortion of the injected grid currents especially in weak grids and rural areas, which can be achieved by many means. This paper deals with the design of a PI-based grid current vector control system for a three-level GCVSC with an actively damped LCL output filter, particularly focusing on practical issues to max out control performance as well as taking requirements concerning current harmonics into account. Appropriate simplifications of the control plant transfer function are applied to enable quick tuning parameter determination while keeping approximation errors low by taking the impact of active damping into account. Overall system performance is assessed in terms of control rise time and injected grid current harmonic distortion. Simulation and experimental results validate the presented control system.