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 (Maranhão, Piauí, Ceará, Rio Grande do Norte, Paraíba, 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, Paraíba, and Pernambuco, whereas the lowest occur in parts of Bahia, Piauí, and Maranhão. 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.

巴西东北部越来越多的风电场投资和安装吸引了该地区的注意，导致投资者和研究人员寻求使用当地风能进行能源生产的更好方法。面对风速时间序列的复杂行为，混合分布模型已应用于双峰数据库，旨在获得最佳的序列拟合模型。本文使用了2004年1月1日至8月29日之间位于巴西东北部地区的九个州（马拉尼昂，皮奥伊，塞阿雷，北里奥格兰德州，帕拉伊巴，伯南布哥州，阿拉戈阿斯，塞尔吉培和巴伊亚州）的数据， 2018年。采用两部分的Wei​​bull分布模型，使用期望最大化（EM）算法对历史序列进行建模，以搜索数据分布中的最佳参数。应用多重分形趋势分析来验证序列随时间的持久性，并使用通过距离反比加权获得的空间化，在缺乏气象风信息的地点估算结果。获得的结果表明，在北里奥格兰德州，帕拉伊巴州和伯南布哥州发现的平均风速最高，而在巴伊亚州，皮奥伊州和马拉尼昂州的部分地区则最低。在每个站点的每天上午10点至晚上8点之间记录了最高平均风速。多重分形分析表明，风速序列在所有站点都表现出持续的整体行为，多重分形主要由小波动引起。对于大多数站点，长期相关性和风速值的广泛概率密度函数均会导致过程的多重分形。这项研究可以确定在巴西东北地区不同位置安装风电场的有利区域。