A hybrid method for the parameter estimation of equivalent wind power plant from accessible measurements

• Ano de defesa: 2025
• Autor(a) principal: Vargas, Paul Junior Zapana
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: estimação de parâmetros; hybrid method; método híbrido; MVMO; parameter estimation; parque eólico,TSM; TSM; wind farm
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/18/18154/tde-27052025-075841/

Wind power generation is a renewable energy source of significant importance and continuous growth, with a high degree of integration into electrical systems. In this context, conducting stability studies through mathematical modeling is essential to represent the behavior of wind farms. However, the mathematical representation of these systems is challenging due to the large number of wind turbines within a wind farm, which often feature diverse characteristics and technologies, resulting in parameter variability, particularly during system disturbances. This dissertation proposes a hybrid method for parameter estimation in both an original equivalent model and a modified equivalent model of a wind farm. The approach combines a metaheuristic algorithm called Mean-Variance Mapping Optimization (MVMO), which provides an intelligent initial estimation, with a non-linear programming algorithm called Trajectory Sensitivity Method (TSM), which refines and finalizes the parameter estimation. The simulation results demonstrate that the hybrid method (MVMO + TSM) is effective and accurate in estimating the parameters of the original equivalent model of a wind farm. However, the results of the modified equivalent model did not achieve a satisfactory parameter estimation.