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Otimização do funcionamento das turbinas eólicas: Reanálise da curva de potência e dos dados meteorológicos
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| 5.98 MB | Adobe PDF |
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Abstract(s)
A energia eólica tem assumido um papel central no panorama energético atual, destacando-se como uma das fontes renováveis mais promissoras. Para garantir a sua eficiência, é essencial monitorizar o desempenho das turbinas eólicas, sendo a curva de potência uma ferramenta fundamental por permitir relacionar a velocidade do vento com a potência gerada e estimar a produção de energia. Neste trabalho, reanalisou-se a curva de potência das turbinas Jer-10 e Jer-11, localizadas em Jandaíra (Brasil), com base em dados recolhidos segundo a norma IEC 61400-12-1. Após definir as condições de medição, com especial atenção no setor de medição, os dados
foram filtrados no software Windographer com base em critérios físicos, geométricos e operacionais. Na fase de tratamento, recorreu-se a um script em Python que consolidou e uniformizou dados de diferentes fontes, automatizando a sincronização temporal dos registos e assegurando a integridade do conjunto analisado — uma mais-valia face a abordagens exclusivamente manuais. Em seguida, os dados foram processados em Microsoft Excel, com correções pela densidade do ar, organização em bins de 0,5 m/s e cálculo da potência média e do coeficiente de potência. Recorreu-se à Distribuição de Weibull para estimar a produção anual de energia (AEP) e avaliar a validade da campanha. Os resultados mostraram que ambas as turbinas ficaram abaixo do desempenho esperado: a Jer-11 apresentou maior estabilidade e proximidade aos valores nominais, enquanto a Jer-10 revelou elevada dispersão e necessidade de ajustes ao setor de medição. Nenhuma das turbinas reuniu dados suficientes para validar a campanha até à velocidade máxima registada, embora se evidencie o rigor metodológico da norma IEC e a robustez da abordagem adotada face a limitações reais, sobretudo no caso da Jer-10.
Wind energy has taken on a central role in the current energy landscape, standing out as one of the most promising renewable sources. Ensuring its efficiency requires monitoring wind turbine performance, with the power curve being a key tool, as it relates wind speed to generated power and enables energy production estimates. This study reanalysed the power curves of the Jer-10 and Jer-11 turbines, located in Jandaíra (Brazil), based on data collected in accordance with the IEC 61400-12-1 standard. After defining the measurement conditions — with a focus on the measurement sector — the data were filtered using Windographer software, applying physical, geometric and operational criteria. During the processing phase, a Python script was used to consolidate and standardize data from various sources, automating the temporal synchronization of records and ensuring the integrity of the dataset — a technical advantage over purely manual approaches. The data were then processed in Microsoft Excel, with air density corrections, binning in 0.5 m/s intervals, and calculation of average power and power coefficient. The Weibull distribution was applied to estimate the annual energy production (AEP) and to assess the campaign’s validity. The results showed that both turbines underperformed: Jer-11 demonstrated greater stability and alignment with nominal values, while Jer-10 exhibited significant dispersion and required adjustments to the measurement sector. Neither turbine collected enough data to validate the campaign up to the maximum recorded wind speed, although the methodological rigor ensured by the IEC standard and the robustness of the adopted approach in addressing real-world limitations — particularly in the case of Jer-10 — were evident.
Wind energy has taken on a central role in the current energy landscape, standing out as one of the most promising renewable sources. Ensuring its efficiency requires monitoring wind turbine performance, with the power curve being a key tool, as it relates wind speed to generated power and enables energy production estimates. This study reanalysed the power curves of the Jer-10 and Jer-11 turbines, located in Jandaíra (Brazil), based on data collected in accordance with the IEC 61400-12-1 standard. After defining the measurement conditions — with a focus on the measurement sector — the data were filtered using Windographer software, applying physical, geometric and operational criteria. During the processing phase, a Python script was used to consolidate and standardize data from various sources, automating the temporal synchronization of records and ensuring the integrity of the dataset — a technical advantage over purely manual approaches. The data were then processed in Microsoft Excel, with air density corrections, binning in 0.5 m/s intervals, and calculation of average power and power coefficient. The Weibull distribution was applied to estimate the annual energy production (AEP) and to assess the campaign’s validity. The results showed that both turbines underperformed: Jer-11 demonstrated greater stability and alignment with nominal values, while Jer-10 exhibited significant dispersion and required adjustments to the measurement sector. Neither turbine collected enough data to validate the campaign up to the maximum recorded wind speed, although the methodological rigor ensured by the IEC standard and the robustness of the adopted approach in addressing real-world limitations — particularly in the case of Jer-10 — were evident.
Description
Keywords
Wind energy power curve wind turbine measurement sector IEC 61400-12-1 Python Windographer Weibull distribution AEP power coefficient bin campaign validation Energia eólica Curva de potência Turbina eólica Setor de medição IEC 61400-12-1 Distribuição de Weibull Coeficiente de potência Validação da campanha
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CC License
Without CC licence