Arquitetura fuzzy hierárquica com defuzzificação adicional de camadas e aplicações ao diagnóstico de qualidade da energia elétrica

Abstract

Among various existing decision-making methods, hierarchical fuzzy methods have emerged as a suitable tool for dealing with complex applications which have many input variables and a high degree of subjectivity. In this context, the product of electric energy stands out. In general, the diagnosis of energy quality is a difficult practice due to the subjectivities inherent to the analysis process, nuances among different standards existing in the world, and uncertainties of evaluation parameters. This thesis proposes a new methodology for the power quality diagnosis based on the hierarchical fuzzy theory with a cascade-type architecture. The proposed method analyzes the quality parameters in steady-state electrical systems based on different existing standards in the world and performs a linguistic/quantitative diagnosis in which the contributions of the analyzed indices are weighted on the power quality of the evaluated system. Firstly, the diagnosis method was implemented from two hierarchical fuzzy architectures known (conventional and defuzzification free). Posteriorly, a new proposed architecture with additional defuzzification of layers was developed to aggregate the main advantages of conventional and defuzzification free in order to make the diagnosis method more complete and robust. This study proposes that the output of each subsystem obtained from primary decision-making process is transferred directly between the hierarchical layers, without loss of linguistic information, to obtain a resultant power quality diagnosis. In addition, a secondary decision-making process is performed together with an additional defuzzification method in order to obtain a complementary specific diagnosis at the out of each hierarchical subsystem. The diagnosis method based on the proposed fuzzy architecture presented satisfactory results when compared with the two existing architectures. After validation of the diagnosis method and hierarchical fuzzy architecture, both presented in this thesis, at the end of research, an wavelet-fuzzy system with generic inference method based on extended overlap functions is proposed as a new tool able of monitoring the power quality in renewable energy systems.