The importance of mean time in power resource assessment for small wind turbine applications

• Correct wind description is crucial in wind power projects.
• We analyze the mean time for small wind turbines resource assessment.
• We develop a wind speed dispersion spectrum analysis at different mean times.
• Different mean times produce different power resource assessments.
• Mean time and turbine selection are key factors in resource assessment.

Wind turbine for low power applications is a clean energy alternative to contribute global warming mitigation. The correct description of wind speeds is crucial to determine the economic viability of a wind power project. The sampling technique used in resource assessment is supported by van der Hoven's work, which concludes that minimum dispersion occurs between 0.1 and 2 hours mean time. International standards for wind turbine power characterization are also based on this work. Here we analyze the influence of using different mean times over data dispersion and wind resource assessment and analyze an adequate mean time for small wind turbine (SWT) applications that contributes to the development of reliable resource assessments. We found a maximum dispersion around 1 minute mean time. The stable wind conditions region was not found in the dispersion analysis presented here. Using this time in SWT resource assessment will detect the largest amount of changes in the time series that may contribute to power production. Resource assessments calculated show that using 1 and 10 minutes as mean times generates power resource assessments with a difference around 17%, which may be a factor that prevents SWT penetration. There exist at least two factors to obtain reliable power resource assessment, the SWT selection and ensemble mean time.