PPGENE PÓS-GRADUAÇÃO EM ENERGIA FUNDAÇÃO UNIVERSIDADE FEDERAL DO ABC Phone: Not available http://propg.ufabc.edu.br/ppgene


Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
DATE: 10/10/2022
TIME: 14:00
LOCAL: Sala 406 do Bloco B do Campus de Santo André da Universidade Federal do ABC

Development of Methodologies for Islanding Detection in Systems with Photovoltaic Generation 

PAGES: 123
AREA: Planejamento Energético

The electric power system is changing. Changes in legislation made it possible to connect distributed generators (DGs) to the grid, and units that only consumed power began to inject it into the grid. Photovoltaic generation has been growing in recent years and is already the most used source as distributed generation in Brazil. One of the main concerns when connecting a DG to the grid is unintentional islanding, which occurs when a portion of the grid containing DG and loads remains electrified, but electrically isolated from the rest of the grid. In this case, the DG power supply is not supervised by the grid. When this situation is not identified by the existing protections, in addition to problems with the quality of energy, accidents can occur, since there is a part of the network that is improperly energized. This work aims to present and compare two methodologies for the detection of islanding of photovoltaic generators. The first methodology is based on artificial neural networks (ANNs), and the detection is carried out through an analysis of the voltage signal at the point of common coupling between the installation considered and the concessionaire. The second proposed methodology uses Discrete Wavelet Transform (DWT) to detect islanding, also analyzing the voltage signal at the connection point with the utility.

At the end of this work, the results of the proposed algorithms in the face of islanding situations are presented, as well as tests to delimit the operating limits of each algorithm, making it possible to verify the good performance of both techniques. The algorithms responded correctly in 100% of the practical cases evaluated, even in situations of low power unbalance. The detection time was low for both techniques, between 0.06 s and 0.09 s.

Presidente - Interno ao Programa - 1545354 - RICARDO CANELOI DOS SANTOS
Membro Titular - Examinador(a) Interno ao Programa - 2236209 - RICARDO DA SILVA BENEDITO
Membro Titular - Examinador(a) Externo ao Programa - 884.651.954-04 - FABIANO FRAGOSO COSTA - UFBA
Membro Suplente - Examinador(a) Interno ao Programa - 1671333 - EDMARCIO ANTONIO BELATI
Membro Suplente - Examinador(a) Externo ao Programa - 1545367 - CELSO SETSUO KURASHIMA
Notícia cadastrada em: 13/09/2022 15:31
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