Cascade Fuzzy Hierarchical System for the Diagnosis of Power Quality
Hierarchical fuzzy systems, diagnosis, power quality, Mamdani inference
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, one can highlight the electric
energy product since the same is evaluated from the different quality indices of one subjective
form. Among different fuzzy hierarchical theories found in literature, the methods
based on conventional and defuzzification-free fuzzy subsystems stand out as being the
most used in applied intelligent systems. Despite this, comparisons that point out the advantages
and disadvantages of these methods are not clearly addressed. Therefore, this
thesis proposes the application of a new hierarchical system fuzzy of the cascade-type
in order to accomplish the power quality diagnosis, from quality indices (total harmonic
distortion of voltage and current, power factor, voltage variation and voltage unbalance).
The performance considering both conventional and defuzzification-free fuzzy hierarchical
methods were satisfactory from the point of view of the application well as the point
of view of the computational burden. The data analyzed were collected at the common
coupling point of a food industry.