The fitting of empirical wind speed distributions is an important component in wind turbine energy yield assessment. Its accuracy depends on both the adequacy of the fitting function and the quality of the fitted wind speed data. Therefore, the system of wind speed distributions was recently introduced to further improve the statistical estimation of wind turbine energy yield. The system of wind speed distributions incorporates the three complement distributions Kappa, Wakeby and Burr-Generalized Extreme Value. In contrast to the progress that has been made in the statistical description of wind speed distributions, there is little research on the effects of poor quality data on the estimation of wind turbine energy yield. Therefore, the goal of this study was to evaluate the robustness of the system of wind speed distributions against typical wind speed data shortcomings such as (1) measurement errors, (2) missing data, and (3) low temporal resolution. The database were quality controlled wind speed time series from 187 measurement stations of the German Meteorological Service located in Germany with a 10-minute temporal resolution. The investigated time series cover the period from July 2016 to December 2017. The original wind speed time series were modified by (1) rounding them to 0.5 m/s and 1 m/s steps, (2) reducing data availability to 1.0% and 0.5%, (3) reducing the temporal resolution. Afterwards, the original and the modified wind speed time series were fitted to the system of wind speed distributions. Ten goodness-of-fit metrics were applied for comparison of the original wind speed distributions with the modified distributions fitted to the system of wind speed distributions. Overall, it was found that the system of wind speed distributions is robust against common quality issues in the wind speed time series. Applying a 2.5 MW wind turbine power curve and fitting the system of wind speed distributions to integer wind speed data, the mean percentage annual energy yield error was estimated at 3.3%. Fitting the system of wind speed distributions to wind speed data with very low data availability of 0.5% of the original data, the annual energy yield over all stations was underestimated at -8.2%. The results of this investigation will help adequately addressing the biases caused by poor quality wind speed data, and thus, help to improve wind turbine energy yield assessment.