Arquitetura fuzzy hierárquica com defuzzificação adicional de camadas e aplicações ao diagnóstico de qualidade da energia elétrica
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...
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| Médium: | doctoralThesis |
| Jazyk: | pt_BR |
| Vydáno: |
Brasil
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| On-line přístup: | https://repositorio.ufrn.br/jspui/handle/123456789/27365 |
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| Shrnutí: | 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. |
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