Multiscale entropy analysis of wind speed dynamics in Petrolina, Northeast Brazil

Abstract

Purpose: In this paper, we analyzed the intra-annual variability of complexity of wind dynamics in Petrolina, Brazil and its relation with the wind potential. Methodology: We applied the Multiscale Sample Entropy (MSE) method on wind speed temporal series for each month of 2010. The data are recorded every 10 min at 50m height. Results: The results showed higher entropy values at higher temporal scales indicating that wind speed fluctuations are les regular and less predictable when wind speed is observed at lower temporal frequency. For all months, average wind speed is above a cut in level 3.5 m, the speed at which turbines start operating and producing electricity, indicating that the location of Petrolina is promising for wind energy generation. We also found that the wind speed is positively correlated with entropy values for all months when recorded at 10min frequency and between August and December when recorded t 1 h frequency. Conclusion: In these periods wind speed temporal fluctuations are more irregular, which is considered as unfavorable condition for the operation of wind turbines, leading to lower efficiency in the capture of wind energy for electricity production.

Method for estimating the wind power micro and minigeneration applied to a city with a subtropical climate in south America

“…In this work we also used the statistical model built from a historical series of wind speeds collected at meteorological stations, as did (Nunes et al, 2021) who used the wind speed data for application in the Markov chain, or as (Silva et al, 2021) who applied the Multiscale Sample Entropy (MSE) method for Petrolina in northeastern Brazil. The purpose of methodology used in our study was not to perform extrapolated forecasts from a few years as is done in the methodology of measure-correlatepredict (MCP) or the Weibull distribution (Weekes et al, 2015).…”