Performance and Reliability of Wind Turbines: A Review

Performance (availability and yield) and reliability of wind turbines can make the difference between success and failure of wind farm projects and these factors are vital to decrease the cost of energy. During the last years, several initiatives started to gather data on the performance and reliability of wind turbines on- and offshore and published findings in different journals and conferences. Even though the scopes of the different initiatives are similar, every initiative follows a different approach and results are therefore difficult to compare. The present paper faces this issue, collects results of different initiatives and harmonizes the results. A short description and assessment of every considered data source is provided. To enable this comparison, the existing reliability characteristics are mapped to a system structure according to the Reference Designation System for Power Plants (RDS-PP®). The review shows a wide variation in the performance and reliability metrics of the individual initiatives. Especially the comparison on onshore wind turbines reveals significant differences between the results. Only a few publications are available on offshore wind turbines and the results show an increasing performance and reliability of offshore wind turbines since the first offshore wind farms were erected and monitored

Enabling Virtual Met Masts for wind energy applications through machine learning-methods

As wind is the basis of all wind energy projects, a precise knowledge about its availability is needed. For an analysis of the site-specific wind conditions, Virtual Meteorological Masts (VMMs) are frequently used. VMMs make use of site calibrated numerical data to provide precise wind estimates during all phases of a wind energy project. Typically, numerical data are used for the long-term correlation that is required for estimating the yield of new wind farm projects. However, VMMs can also be used to fill data gaps or during the operational phase as an additional reference data set to detect degrading sensors. The value of a VMM directly depends on its ability and precision to reproduce site-specific environmental conditions. Commonly, linear regression is used as state of the art to correct reference data to the site-specific conditions. In this study, a framework of 10 different machine-learning methods is tested to investigated the benefit of more advanced methods on two offshore and one onshore site. We find significantly improving correlations between the VMMs and the reference data when using more advanced methods and present the most promising ones. The K-Nearest Neighbors and AdaBoost regressors show the best results in our study, but Multi-Output Mixture of Gaussian Processes is also very promising. The use of more advanced regression models lead to decreased uncertainties; hence those methods should find its way into industrial applications. The recommended regression models can serve as a starting point for the development of end-user applications and services

Windenergie Report Deutschland 2018

Der Windenergiereport des Fraunhofer-Instituts für Energiewirtschaft und Energiesystemtechnik (IEE) berichtet wissenschaftlich und anschaulich über die jährliche Entwicklung der Windenergie. Der Zubau und Ertrag von On- und Offshore Windenergieanlagen, der Anteil der Windenergie im Strommix, die Netzintegration und die Schritte zum Netzausbau werden übersichtlich dargestellt. Special Reports informieren über aktuelle Themen und Trends der Branche