Application of Particle Dampers on a Scaled Wind Turbine Generator to Improve Low-Frequency Vibro-Acoustic Behavior

The purpose of this paper is to introduce a honeycomb damping plate (HCDP) concept based on the particle damping technique to reduce the low-frequency vibration response of wind turbine generators. The HCDP cells contain granular materials and are mounted at different positions on the generator to reduce the transmission of vibrations from stator ring to stator arm. To investigate the efficiency of the HCDP concept in the laboratory, a small-scale replica inspired by the original wind turbine generator is used as reference geometry. The efficiency of the vibration attenuation by using the HCDP concept is experimentally investigated with the help of a laser scanning vibrometer device. In this contribution, the influence of four different granular materials on the vibration attenuation is experimentally investigated. Furthermore, the influence of HCDP positioning on the transmission path damping is analyzed. Apart from this, the effect of single-unit (SU) and multi-unit (MU) HCDP on the frequency response of the generator is also studied. The experimental approach in this paper shows good damping properties of the HCDP concept for reducing the vibration amplitude.

HESS in a Wind Turbine Generator: Assessment of Electric Performances at Point of Common Coupling with the Grid

Among Renewable Energy Sources (RES), wind energy is emerging as one of the largest installed renewable-power-generating capacities. The technological maturity of wind turbines, together with the large marine wind resource, is currently boosting the development of offshore wind turbines, which can reduce the visual and noise impacts and produce more power due to higher wind speeds. Nevertheless, the increasing penetration of wind energy, as well as other renewable sources, is still a great concern due to their fluctuating and intermittent behavior. Therefore, in order to cover the mismatch between power generation and load demand, the stochastic nature of renewables has to be mitigated. Among proposed solutions, the integration of energy storage systems in wind power plants is one of the most effective. In this paper, a Hybrid Energy Storage System (HESS) is integrated into an offshore wind turbine generator with the aim of demonstrating the benefits in terms of fluctuation reduction of the active power and voltage waveform frequency, specifically at the Point of Common Coupling (PCC). A MATLAB®/SimPowerSystems model composed of an offshore wind turbine interfaced with the grid through a full-scale back-to-back converter and a flywheel-battery-based HESS connected to the converter DC-link has been developed and compared with the case of storage absence. Simulations were carried out in reference to the wind turbine’s stress conditions and were selected—according to our previous work—in terms of the wind power step. Specifically, the main outcomes of this paper show that HESS integration allows for a reduction in the active power variation, when the wind power step is applied, to about 3% and 4.8%, respectively, for the simulated scenarios, in relation to more than 30% and 42% obtained for the no-storage case. Furthermore, HESS is able to reduce the transient time of the frequency of the three-phase voltage waveform at the PCC by more than 89% for both the investigated cases. Hence, this research demonstrates how HESS, coupled with renewable power plants, can strongly enhance grid safety and stability issues in order to meet the stringent requirements relating to the massive RES penetration expected in the coming years.

Possibility of Power Electronics-Based Control Analysis of a Self-Excited Induction Generator (SEIG) for Wind Turbine and Electrolyzer Application

A self-excited induction generator (SEIG) is very simple and robust, has a reduced unit size, is easy to implement and simple to control, and requires very little maintenance compared to other types of generators. In variable operating conditions, the SEIG requires a power electronics interface to transform from the variable frequency voltage output of the generator to a battery voltage output or the related applications. In our study, we tied the SEIG to the power electronics system comprising a diode rectifier and DC/DC converter, and then a final DC load for fuel cell applications was connected. An example of such an application is an electrolyzer where an equivalent circuit is modeled for use in this study. To accomplish the proposed system, we utilized PSCAD and MATLAB for its simulation, control, and analysis. A new system configuration considering three different wind speeds and breaker conditions is modeled and analyzed. The results show that the suggested strategies in this study would contribute to designing and analyzing a more practical power electronics interface system for a wind turbine generator with a DC load.

Azure machine learning studio and SCADA data for failure detection and prediction purposes: A case of wind turbine generator

Most industrial systems have supervisory control and data acquisition (SCADA) systems that collect and store process parameters. SCADA data is seen as a valuable source to get and extract insights about the asset health condition and associated maintenance operations. It is still unclear how appliable and valid insights SCADA data might provide. The purpose of this paper is to explore the potential benefits of SCADA data for maintenance purposes and discuss the limitations from a machine learning perspective. In this paper, a two-year SCADA data related to a wind turbine generator is extracted and analysed using several machine learning algorithms, i.e., two-class boosted decision tree, two-class decision forest, k-means clustering on Azure ML learning studio. It is concluded that the SCADA data can be useful for failure detection and prediction once rich training data is given. In a failure prediction context, data richness means ensuring that fault features are presented in the training data. Moreover, the logs file can be used as labelled data to supervise some algorithms once they are reported in a more rigorous manner (timing, description).