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Power converters in wind turbines exhibit frequent failures, the causes of which have remained unexplained for years. Field-experience based research has revealed that power- and thermal-cycling induced fatigue effects in power electronics do not contribute significantly to the field failures observed. Instead, clear seasonal failure patterns point to environmental influences, in particular to humidity, as a critical stressor and likely driver of converter failure. In addition to the electrical operating conditions, it is therefore important to also identify and characterize the climatic conditions that power converters in wind turbines are exposed to, both as a contribution to root-cause analysis and as a basis for the derivation of suitable test procedures for reliability qualification of components and systems. This paper summarizes the results of field-measurement campaigns in 31 wind turbines of seven different manufacturers spread over three continents. The temperature and humidity conditions inside the converter cabinets are characterized and related to the environmental conditions of the turbines and to their operation. The cabinet-internal climate is found to be subject to pronounced seasonal variations. In addition to the site-specific ambient climatic conditions and the operation of the turbines, the converter cooling concept is identified to significantly influence the climatic conditions inside the power cabinets. [ABSTRACT FROM AUTHOR]

In wind energy site assessment, one major challenge is to represent both the local characteristics as well as general representation of the wind climate on site. Micro-scale models (e.g., Reynolds-Averaged-Navier-Stokes (RANS)) excel in the former, while meso-scale models (e.g., Weather Research and Forecasting (WRF)) in the latter. This paper presents a fast approach for meso–micro downscaling to an industry-applicable computational fluid dynamics (CFD) modeling framework. The model independent postprocessing tool chain is applied using the New European Wind Atlas (NEWA) on the meso-scale and THETA on the micro-scale side. We adapt on a previously developed methodology and extend it using a micro-scale model including stratification. We compare a single- and multi-point downscaling in critical flow situations and proof the concept on long-term mast data at Rödeser Berg in central Germany. In the longterm analysis, in respect to the pure meso-scale results, the statistical bias can be reduced up to 45% with a single-point downscaling and up to 107% (overcorrection of 7%) with a multi-point downscaling. We conclude that single-point downscaling is vital to combine meso-scale wind climate and micro-scale accuracy. The multi-point downscaling is further capable to include wind shear or veer from the meso-scale model into the downscaled velocity field. This adds both, accuracy and robustness, by minimal computational cost. The new introduction of stratification in the micro-scale model provides a marginal difference for the selected stability conditions, but gives a prospect on handling stratification in wind energy site assessment for future applications. [ABSTRACT FROM AUTHOR]