Banca de DEFESA: GUTENBERG FERREIRA DA SILVA

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : GUTENBERG FERREIRA DA SILVA
DATE: 05/06/2023
TIME: 14:00
LOCAL: meet.google.com/ngm-bdva-oqr
TITLE:

The intra-annual variability of wind speed complexity

KEY WORDS:

Wind speed; Sample entropy; Multiscale Entropy; Lacunarity

PAGES: 47
BIG AREA: Ciências Agrárias
AREA: Agronomia
SUMMARY:

Among renewable energy sources, Wind energy is one of the fastest growing in recent decades due to its high efficiency and low pollution. As a producer of wind energy, Brazil ranks sixth in the world, behind China, USA, Germany, India and France. The assessment of wind potential at a given location requires a detailed statistical analysis of wind speed and its frequency distribution at different times and different periods of the year. However, due to the intermittency and high space-time variability of wind speed, the large-scale integration of wind energy in to the electrical grid is still a challenging task. The knowledge of the temporal organization (complexity) of wind speed can provide information about underlying stochastic processes that can be used for planning wind energy production and for developing and evaluating predictive models of wind speed and wind potential. In this work, the intra-annual temporal variability of the wind speed complexity at 50 m height in Petrolina was analyzed, by using the methods Sample Entropy (SampEn), Multiscale entropy (MSE) and Lacunarity. The Sample Entropy method evaluates the regularity of the time series, the Multiscale entropy method was developed as the generalization of Sample Entropy to analyze the complexity of non-stationary time series considering multiple time scales, and the Lacunarity method evaluates the distribution of gaps in a set of data. The Sample Entropy results showed that the period between 10h and 12h is more favorable for wind energy generation: in this period the wind speed values are higher (indicating higher wind potential) and SampEn values are lower (indicating more regular dynamics). Multiscale entropy analysis showed that for a 10-minute frequency wind speed and entropy are positively correlated, while for a 1-hour frequency a positive correlation is observed between August and December. Lacunarity analysis results showed that September is the month with the most favorable conditions for wind power generation indicated by the highest average speed and lowest lacunarity.

COMMITTEE MEMBERS:
Interno - ANTONIO SAMUEL ALVES DA SILVA
Externo à Instituição - IKARO DANIEL DE CARVALHO BARRETO - UFPE
Interno - LUCIAN BOGDAN BEJAN
Externo à Instituição - SILVIO FERNANDO ALVES XAVIER JUNIOR - UEPB
Presidente - TATIJANA STOSIC