The compact design of modern wind farms means that turbines are located in the wake over a certain amount of time. This leads to reduced power and increased loads on the turbine in the wake. Currently, research has been dedicated to reduce or avoid these effects. One approach is wake-steering, where a yaw misalignment is introduced in the upstream wind turbine. Due to the intentional misalignment of upstream turbines, their wake flow can be forced around the downstream turbines, thus increasing park energy output. Such a control scheme reduces the turbulence seen by the downstream turbine but introduces additional load variation to the turbine that is misaligned. Within the scope of this investigation, a generic multi body simulation model is simulated for various yaw misalignments. The time series of the calculated loads are combined with the wind speed distribution of a reference site over 20 years to investigate the effects of yaw misalignments on the turbines main bearing loads. It is shown that damage equivalent loads increase with yaw misalignment within the range considered. Especially the vertical in-plane force, bending and tilt moment acting on the main bearing are sensitive to yaw misalignments. Furthermore, it is found that the change of load due to yaw misalignments is not symmetrical. The results of this investigation are a primary step and can be further combined with distributions of yaw misalignments for a study regarding specific load distributions and load cycles.

Due to the increasing cost pressure, a reliable wind turbine (WT) is a major priority. Reliability is strongly determined by the design process. Therefore, it is required to have accurate and reliable load assumptions, ideally at early stages of the design process. This contribution discusses suitable simulation methods and then presents the validation of the load calculation of a full scale multi-body simulation (MBS) wind turbine model. The modelled wind turbine is a 2.75 MW generic research wind turbine. The aim of the MBS model is to produce load time series of components' internal loads and states. These results are used as input for more detailed component models e.g. finite element (FE) tooth contact or bearing models. Subsequently these are then used to calculate detailed bearing and tooth loads. The MBS is compared with comprehensive measurements from a full scale 4 MW system test bench. The main focus will be on the validation of the MBS models internal components loads, displacements and deformations on which the bearing and tooth loads depend on. It is evaluated which model parameters influence these calculation objectives as well as the modelling methods and fidelity required to represent them meaningfully. It is evaluated that model and test indicated comparable results.

Each wind turbine experiences a variety of loads during its lifetime, especially inside a wind farm due to the wake effect between the turbines. This paper describes a possibility to observe a load spectrum while considering wake effects in a wind farm by through the turbulence intensity. The turbulence intensity is distributed along the wind rose of Alpha Ventus. For each turbulence intensity, a Weibull characteristic is calculated. The resulting wind fields are used to determine the loads through a multibody simulation of an imaginary wind turbine located at FINO-1, representing a closely placed wind turbine at the outer edge of a wind farm. These loads are analyzed and summed up. As expected, the change of the turbulence intensity due to the wake effect has an impact on the internal loading of a wind turbine inside a wind farm. Based on the assumed loading conditions, the maximum loads increased by a factor of almost 2.5.

Many energy supply systems around the world are currently undergoing a phase of transition characterized by a continuing increase in installed renewable power generation capacities. The inherent volatility and limited predictability of renewable power generation pose various challenges for an efficient system integration of these capacities. One approach to manage these challenges is the deployment of small-scale dispatchable power generation and storage units on a local level. In this context, gas turbine cogeneration units, which are primarily tasked with the provision of power and heat for industrial consumers, can play a significant role, if they are equipped with a sufficient energy storage capacity allowing for a more flexible operation. The present study investigates a system configuration, which incorporates a heat-driven industrial gas turbine interacting with a wind farm providing volatile renewable power generation. The required energy storage capacity is represented by an electrolyzer and a pressure vessel for intermediate hydrogen storage. The generated hydrogen can be reconverted to electricity and process heat by the gas turbine. The corresponding operational strategy for the overall system aims at an optimal integration of the volatile wind farm power generation on a local level. The study quantifies the impact of selected system design parameters on the quality of local wind power system integration, that can be achieved with a specific set of parameters. In addition, the impact of these parameters on the reduction of CO2 emissions due to the use of hydrogen as gas turbine fuel is quantified. In order to conduct these investigations, detailed steady-state models of all required system components were developed. These models enable accurate simulations of the operation of each component in the complete load range. The calculation of the optimal operational strategy is based on an application of the dynamic programming algorithm. Based on this model setup, the operation of the overall system configuration is simulated for each investigated set of design parameters for a one-year period. The simulation results show that the investigated system configuration has the ability to significantly increase the level of local wind power integration. The parameter variation reveals distinct correlations between the main design parameters of the storage system and the achievable level of local wind power integration. Regarding the installed electrolyzer power consumption capacity, smaller additional benefits of capacity increases can be identified at higher levels of power consumption capacity. Regarding the geometrical volume of the hydrogen storage, it can be determined that the storage volume loses its limiting character on the operation of the electrolyzer at a characteristic level. The additional investigation of the CO2 emission reduction reveals a direct correlation between the level of local wind power integration and the achievable level of CO2 emission reduction.

Wind energy is an essential source of renewable energy. However, to compete with conventional energy sources, energy needs to be produced at low costs. An ideal situation would be to have no costly, unscheduled maintenance, preferably. Currently, O&M are half of the yearly expenses. The O&M costs are kept low by scheduled and reactive maintenance. An alternative is predictive maintenance. This method aims to act before any critical and costly repair is required. Additionally, the component is used to its full potential. However, such a strategy requires a damage indication, similar to one provided by a condition monitoring system (CMS). This paper investigates if Supervisory Control and Data Acquisition (SCADA) can be used as a damage indicator and CMS. Since 2006, every wind turbine is obliged to use such a SCADA-system. SCADA records a 10-minute average, maximum, minimum, and standard deviation of multiple technical information channels. Analytics can use those data to determine the normal behavior and a prediction model of the wind turbine. The authors investigated statistical and data mining methods to predict main bearing faults. The methods indicated a defect of up to 6 months before its maintenance.