Estudo de modelos e técnicas de detecção e diagnóstico de falhas aplicados a sistemas fotovoltaicos

• Autor(a) principal: Maia, Flávio Couvo Teixeira
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFS
• Texto Completo: http://ri.ufs.br/jspui/handle/riufs/12317
• topic: Engenharia elétrica; Sistemas de energia fotovoltaica; Diodos; Fontes alternativas de energia; Energia solar; Modelos elétricos; Diodo; Perdas por descasamento elétrico; Detecção e diagnóstico de falhas; Método k-NN; Photovoltaic systems; Electric models; Diode; Mismatch losses; Fault Detection and Diagnosis; k-NN method; ENGENHARIAS::ENGENHARIA ELETRICA

Demand for electricity has been growing year by year, raising concern about nonrenewable energy generation matrices that cause environmental degradation. For this reason, several countries have been investing in renewable sources, especially photovoltaic sources, which uses photovoltaic modules to convert the solar radiation into electric power. As in any electrical system, these systems are also susceptible to failure, which can lead to power losses or even interruption. Photovoltaic monitoring systems becomes an important ally, as it allows identifying problems, minimizing losses. For this purpose, a good mathematical model, which accurately represents the modules, enables the identification of faults by comparing the measured and simulated signals. In this sense, this work presents, firstly, a study of mathematical models, applicated in a system composed of three modules in series, using parameter from two sources so the most accurate parameters can be defined: modules datasheet and an I-V curve analyzer. Then, from the study of fault detection and diagnosis techniques in photovoltaic systems, the k- NN method was chose as a tool to diagnose and identify the modules affected by shading, taking advantage of the mismatch effect, which make them electrically different from each other. The results showed that it is possible to identify modules affected by shadows, individually or in groups, and the type of shadow (partial or total).