Banca de QUALIFICAÇÃO: MARIA IZABEL DA SILVA GUERRA

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : MARIA IZABEL DA SILVA GUERRA
DATE: 08/12/2020
TIME: 15:00
LOCAL: Sala virtual Google Meet
TITLE:

Study of ANFIS controller for tracking the maximum power point of photovoltaic systems

KEY WORDS:

ANFIS, MPPT, MPP, Photovoltaic System, Buck-boost converter

PAGES: 185
BIG AREA: Engenharias
AREA: Engenharia Elétrica
SUBÁREA: Eletrônica Industrial, Sistemas e Controles Eletrônicos
SPECIALTY: Controle de Processos Eletrônicos, Retroalimentação
SUMMARY:

Photovoltaic (PV) systems have shown growth in the world's electrical matrix. However, the non-linear nature of PV arrangements and their strong dependence on environmental conditions decrease the maximum power they can produce and, consequently, reduce their performance and commercial attractiveness. Maximum Power Point Tracking (MPPT) techniques have been studied over the years to minimize these problems. Among the various control techniques used for spot tracking and maximum power, the optimized Adaptive Neuro Fuzzy Inference System (ANFIS) technique has not been much explored, although it has a high potential for use. After all, it combines the neural network's ability to process a set of numerical data with the ease of fuzzy logic in describing a system linguistically. Therefore, the present work proposes to develop MPPT techniques based on ANFIS to be applied to PV systems that have the buck-boost as a CC-CC converter. The developed ANFIS architectures differ from the input variables, which are divided into environmental and electrical parameters. To assist in the study of ANFIS performance, four PV systems composed of PV array, buck-boost converter, MPPT, and load were modeled. The systems differed by the total power of the system. Three different control algorithms were also modeled (Perturb and Observe, Artificial Neural Network and Fuzzy Controller), in addition to ANFIS. Through the preliminary analyzes, it was noticed that, with the irradiance and ambient temperature variables, the system with MPPT based on ANFIS showed lower tracking speed and greater precision in reaching the MPP than the Perturb e Observe and Fuzzy Controller techniques. In contrast, its performance was similar to that of the Artificial Neural Network. Regarding the use of electrical parameters as input variables, the performance of ANFIS presented superior results in some conditions, but in other situations, inferior results than the other techniques used as MPPT. Given the above, the study of the use of other electrical parameters is underway or even to mix the use of environmental and electrical parameters as input variables of MPPT based on ANFIS.

BANKING MEMBERS:
Presidente - 1451883 - FABIO MENEGHETTI UGULINO DE ARAUJO
Interno - 1149567 - ANDRES ORTIZ SALAZAR
Interno - 350693 - ANDRÉ LAURINDO MAITELLI
Externo à Instituição - JOÃO TEIXEIRA DE CARVALHO NETO - IFRN
Externo à Instituição - MARCELO ROBERTO BASTOS GUERRA VALE - UFERSA