Wind turbine modeling, maximum power point tracking (MPPT), and experimental validation

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hdl:2117/415420
Document typeArticle
Defense date2024-07-29
Rights accessOpen Access
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ProjectDESARROLLO Y VALIDACION DE ESTRATEGIAS DE APRENDIZAJE PROFUNDO Y AUTOMATICO PARA EL MANTENIMIENTO PREDICTIVO Y DETECCION TEMPRANA DE DAÑOS ESTRUCTURALES EN AEROGENERADORES (AEI-PID2021-122132OB-C21)
Gemelos digitales para la monitorización de la condición de aerogeneradores (AEI-TED2021-129512B-I00)
Gemelos digitales para la monitorización de la condición de aerogeneradores (AEI-TED2021-129512B-I00)
Abstract
The research presented is driven by the global increase in wind power capacity and the commitment of the scientific community to facilitate its integration into electrical grids. The focus of this study is the modeling of a wind turbine system, beginning with its mechanical components. To ensure the production of power at optimal levels, a control strategy for Maximum Power Point Tracking (MPPT) based on Optimal Torque (OT) has been adopted. The model and control method, developed in Matlab/Simulink, have demonstrated their precision and efficacy through experimental verification using SCADA data acquired from an operational wind turbine.
CitationEben, I. [et al.]. Wind turbine modeling, maximum power point tracking (MPPT), and experimental validation. "Renewable energy and power quality journal", 29 Juliol 2024, vol. 22, núm. 2, p. 1-6.
ISSN2172-038X
Publisher versionhttps://repqj.com/index.php/repqj/article/view/3923
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