Mixture distribution and multifractal analysis applied to wind speed in the Brazilian Northeast region

The growing investments and installations of wind farms in the Brazilian Northeast have drawn attention to the region, leading investors and researchers to seek better ways of using the local wind regimen for energy production. In face of the complex behavior of wind speed time series, mixture distribution models have been applied to bimodal databases aiming at achieving the best modeling for series fitting. This paper used data from stations located in the nine states that make up the Brazilian Northeast region (Maranhao, Piaui, Ceara, Rio Grande do Norte, Paraiba, Pernambuco, Alagoas, Sergipe, and Bahia) between January 1st, 2004 and August 29th, 2018. The two-component Weibull distribution model was employed to model the historical series using the Expectation Maximization (EM) algorithm to search for optimal parameters in data distribution. Multifractal detrended fluctuation analysis was applied to verify series persistence over time and, using spatialization obtained with inverse distance weighting, the results were estimated at the sites lacking meteorological wind information. The results obtained indicate that the highest mean wind speeds are found in the states of Rio Grande do Norte, Paraiba, and Pernambuco, whereas the lowest occur in parts of Bahia, Piaui, and Maranhao. The highest mean wind speeds were recorded between 10 a.m. and 8 p.m. of each day at every station. Multifractal analysis revealed that wind speed series exhibit persistent overall behavior for all stations, with multifractality dominated by small fluctuations. For most of the stations both long term correlations and broad probability density function of wind speed values are found to cause multifractality of the process. This study allows identifying favorable areas for the installation of wind farms in different locations of the Brazilian Northeast region.