A cascade-type hierarchical fuzzy system with additional defuzzification of layers for the automatic power quality diagnosis

• Published in: Applied soft computing Vol. 80; pp. 657 - 671
• Main Authors: Nolasco, Diego H.S., Palmeira, Eduardo S., Costa, Flavio B.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2019
• Subjects: Additional defuzzification of layers; Aggregate fuzzy inference; Diagnosis; Hierarchical fuzzy system; Power quality; Hierarchical fuzzy system; Additional defuzzification of layers; Diagnosis; Power quality; Aggregate fuzzy inference
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2019.02.007

In general the power quality diagnosis of low-voltage electrical systems is a difficult issue due to the nuances of different power quality standards around the world and the uncertainties characteristics of the evaluation parameters. In this framework, this paper proposes a new methodology for diagnosing the power quality by means of a cascade-type hierarchical fuzzy system with additional defuzzification of layers (C-HFS-ADL), also proposed in this paper, which is able to evaluate the power quality indices in steady-state (total harmonic distortions, power factor, voltage variation, and unbalance voltage) considering several standards and provides a proper and complete diagnosis of the power quality in electrical systems. In the proposed C-HFS-ADL each output of a subsystem is transferred between inner layers of the hierarchical system and a total power quality diagnosis is obtained taking into account a secondary decision-making process with additional defuzzification in order to have a partial diagnosis for each subsystem of the hierarchy. In addition, an algorithm was implemented considering different inference systems for analyzing the behavior of the C-HFS-ADL on a database (power quality indices) obtained from a real electrical substation for observing its advantages and disadvantages. •A hierarchical fuzzy system with additional defuzzification of layers is proposed.•A power quality diagnosis method in the steady-state condition is also proposed.•A generic-aggregate fuzzy inference method is implemented in the PQ diagnosis method.•The proposed power quality diagnosis method was evaluated considering actual data.