Submitted by Emanuel Varela Cardoso (emanuel.varela@ufcg.edu.br) on 2018-08-15T18:07:08Z No. of bitstreams: 1 LEANDRO RODRIGUES DE SOUZA – TESE (PPGMet) 2016.pdf: 12492309 bytes, checksum: ca8d5277ca89e54d596cec75ed55a590 (MD5) / Made available in DSpace on 2018-08-15T18:07:08Z (GMT). No. of bitstreams: 1 LEANDRO RODRIGUES DE SOUZA – TESE (PPGMet) 2016.pdf: 12492309 bytes, checksum: ca8d5277ca89e54d596cec75ed55a590 (MD5) Previous issue date: 2016-07-14 / CNPq / O objeto principal do estudo foi utilizar o modelo de mesoescala BRAMS (versão 5.2) para simular a variabilidade do vento, para fins de caracterização e avaliação de potencial eólico nos Estados do Amapá e Pará (Brasil) no ano de 2009. Na metodologia foram aplicados índices e um refinamento estatístico com Redes Neurais Artificiais (RNAs) para validação e ajustes das saídas do modelo atmosférico. Nos resultados, verificou-se que no Amapá a velocidade média do vento horária foi maior em Macapá com valor igual a 3,23 m/s e; no Pará foi em Soure com 2,61 m/s. Macapá (AP) e Soure (PA) apresentaram, também, os valores mais expressivos em escala mensal da velocidade do vento com 3,23 e 3,00 m/s, respectivamente. Após a simulação e uso RNAs, percebeu um aumento estatisticamente significativo da simulação numérica, que aumenta a credibilidade do BRAMS na caracterização da variabilidade do vento nas diversas escalas de tempo. Além disso, na análise da direção do vento, o modelo de mesoescala mostrou-se eficiente sua representação em todas as estações, inclusive com suas magnitudes, exceto em Belém que a direção predominante medida foi de Leste e o modelo reproduziu de Nordeste. No estudo de casos para períodos chuvoso e seco, foram observados que tanto o BRAMS como as RNAs representaram a variabilidade da intensidade do vento de forma eficiente em todas as estações, exceto Macapá que o modelo atmosférico subestimou. Os índices estatísticos (BIAS, RMSE e r) aplicados nas comparações dos dados foram satisfatórios para o BRAMS e com as RNAs, no qual o coeficiente de Pearson na maioria das estações mostrou correlações Moderadas (0,40 a 0,69) alcançando correlações Muito Forte (0,90 a 1,0). Em seguida, os parâmetros da FDP de Weibull indicaram que BRAMS melhor simulou o parâmetro de Forma na maioria dos pontos de estudo e, as RNAs mostraram representação mais adequada do parâmetro de Escala. Por tanto, a utilização de modelos atmosféricos para simular a variabilidade do vento é uma ferramenta importante, principalmente, quando não há registros de dados observacionais em locais remotos. E, se tratando de Amazônia, a baixa densidade de estações meteorológicas na extensa região inviabiliza estudos detalhados para monitorar locais com potencial eólico. / The main objective of the study was to use the mesoscale model BRAMS (version 5.2) to simulate the wind variability, for the purpose of characterization and wind potential evaluation in the states of Amapá and Pará (Brazil) in 2009. The methodology was applied indexes and statistical refinement with Artificial Neural Networks (ANN) for validation and adjustments of the atmospheric model outputs. As a result, it was found that in Amapá average wind speed time was higher in Macapá with a value of 3,23 m/s and; Pará was in Soure with 2,61 m/s. Macapa (AP) and Soure (PA) had also the higher values in wind speed of the monthly scale with 3,23 and 3,00 m/s, respectively. After the ANN and use simulation to validation, noticed a statistically significant increase in numerical simulation, which increases the credibility of BRAMS to characterize the wind variability in different time scales. Moreover, the wind direction analysis, the mesoscale model was efficient representation in all seasons, including their magnitudes, except in Bethlehem to the predominant direction as was East and reproduced Northeast model. In the case studies for rainy and dry seasons, it was observed that both the BRAMS as ANN represented the wind speed variability efficiently in all seasons except Macapa that atmospheric model underestimated. Statistical indices (BIAS, RMSE and r) applied to the data comparisons were satisfactory for BRAMS and the ANN in which the Pearson coefficient of at most stations showed moderate correlations (0,40-0,69) reaching correlations very strong (0.90-1,0). Then, the PDF Weibull parameters indicated that BRAMS best simulated shape parameter in most points of study and the RNAs showed better representation of the scale parameter. Therefore, the use of atmospheric models to simulate the wind variability is an important tool, especially when there are no records of observational data in remote locations. And, in the case of Amazon, the low density of weather stations in the vast region derail detailed studies to monitor places with wind potential.

L’intégration croissante de la production éolienne ne participant pas au réglage de fréquence induit de nouvelles difficultés de gestion des systèmes électriques. Ces problèmes sont d’autant plus significatifs que le réseau est faible. La présente thèse vise à évaluer la performance et la fiabilité du réglage de fréquence des éoliennes à l’échelle du système électrique. Les études sont appliquées sur un réseau insulaire.D’abord, l’impact d’un fort taux de pénétration de la production éolienne sur l’allocation de la réserve primaire et sur le comportement dynamique du réseau est caractérisé. Il est montré que la participation des éoliennes au réglage de fréquence est techniquement indispensable pour le maintien de la sûreté du système électrique à partir d’un certain taux de pénétration. Deux solutions permettant aux éoliennes de contribuer au réglage de fréquence sont ensuite étudiées par simulations dynamiques. La performance d’une inertie émulée est caractérisée en considérant l’impact du point de fonctionnement initial des éoliennes et des paramètres du contrôleur. La contribution de la réserve éolienne à l’amélioration de la performance dynamique du système est également identifiée.Afin d’évaluer le potentiel et la fiabilité de la réserve éolienne, la dernière partie de ce travail est consacrée aux études statistiques prenant en compte la variabilité et l’incertitude de la prévision de la production. Deux stratégies du placement de réserve sont proposées et comparées. L’impact des erreurs de prévision sur le potentiel de réserve éolienne est également mis en évidence. Enfin l’énergie réglante d’une ferme et la plage de réglage du statisme éolien sont caractérisées / The increasing development of wind power that does not participate in frequency control leads to new challenges in the management of electrical power systems. The problems are more significant in weak power grids. The present thesis aims to evaluate the performance and the reliability of frequency response from wind turbines on a system-wide scale. Studies are applied onto an isolated power grid.First of all, the impact of high levels of wind penetration on primary reserve allocation and on grid dynamic behaviour is characterized. It is shown that the participation of wind turbines in frequency regulation is technically required for maintaining power system security from a certain wind penetration rate.Two solutions allowing wind turbines to contribute to frequency control are then studied through dynamic simulations. The performance of emulated inertia is characterized by taking into account the impact of initial wind operating point and controller parameters. The contribution of wind power reserve to system dynamic performance improvement is also identified.In order to assess the potential and the reliability of wind primary reserve, the last part of this research work is devoted to statistical analyses considering the variability and the prediction uncertainty of wind generation. Two strategies for reserve allocation are proposed and compared. The impact of forecast errors on the potential of wind power reserve is also highlighted. Finally the power frequency characteristic of a wind farm as well as the droop adjustment range is characterized

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