AVALIAÇÃO PROBABILÍSTICA DO IMPACTO DA GERAÇÃO DISTRIBUÍDA EÓLICA NOS AFUNDAMENTOS DE TENSÃO DE CURTA DURAÇÃO.

• Ano de defesa: 2012
• Autor(a) principal: SILVA, Tiago Alencar
• Orientador(a): RODRIGUES, Anselmo Barbosa
• Banca de defesa: RODRIGUES, Anselmo Barbosa , Silva, Maria da Guia da, BORGES, Carmen Lúcia Tancredo, RESENDE, Leonidas Chaves
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: Redes de distribuição de energia; Qualidade de energia; Geração Distribuída; Afundamento de tensão; Geração Eólica; Simulação Monte Carlo; Métodos Probabilísticos
• Palavras-chave em Inglês: Power Distribution Network; Power Quality; Distributed Generation; Voltage Sag; Wind Energy Generation; Monte Carlo Simulation; Probabilistic Methods
• Área do conhecimento CNPq: Conversão e Retificação da Energia Elétrica
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/1875

The Distributed Generation (DG) can improve the power quality indices associated with Short Duration Voltage Variations (SDVV) due to the reduction in the electric network loading, which in turn causes an improvement in the pre-fault voltage profile. On the other hand, the DG can also deteriorates the power quality indices related to SDVV due to the increasing in the fault currents, which in turns reduce the post-fault voltages. Furthermore, the assessment of the DG impact on SDVV is more difficult with the presence of renewable energy resources. This complexity is due to fluctuations in output power caused by stochastic variations in the primary energy source (sun, wind, tide levels, etc.). Additionally, the bibliographical review on Predictive Assessment of Short Duration Voltage Variations (PAVV) revealed that none of the existing methodology considered the impact of fluctuations in the output power of a wind DG on power quality indices related to SDVV. It was also noticed that the load variations during the study period are ignored in the papers on SDVV. The existence of these deficiencies and the governmental incentives for the use of wind generation motivated this research. The main aim of this dissertation is the development of a methodology for the PAVV capable of recognizing uncertainties associated with wind DG and load fluctuations. The modeling of these uncertainties was carried out using NonSequential Monte Carlo Simulation (MCS). The nodal voltages in the fault scenarios generated by MCS were evaluated using the Admittance Summation Method (ASM) in phase coordinates. The combination of the MCS with the ASM allowed estimating the following indices related to SDVV: the expected value of the SARFI (“System Average RMS – Variation – Frequency Index”) and expected nodal frequency of SDVV. Furthermore, the probability distributions and box plots of the SARFI index have been obtained. The proposed method for the PAVV was tested and validated in a test system with 32 buses. The tests results demonstrated that the DG insertion causes an improvement in the power quality indices associated with SDVV. Additionally, the substitution of conventional DG by wind DG cause a small deterioration in the power quality indices related to SDVV due to fluctuations in the output power of the wind DG. Finally, it was observed that the load fluctuations during the study period cause significant variations in the SARFI index.