EGFC: Evolving Gaussian Fuzzy Classifier from Never-Ending Semi-Supervised Data Streams - With Application to Power Quality Disturbance Detection and Classification

Daniel Leite, Leticia Decker, Marcio Santana, Paulo Souza

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Power-quality disturbances lead to several drawbacks such as limitation of the production capacity, increased line and equipment currents, and consequent ohmic losses; higher operating temperatures, premature faults, reduction of life expectancy of machines, malfunction of equipment, and unplanned outages. Real-time detection and classification of disturbances are deemed essential to industry standards. We propose an Evolving Gaussian Fuzzy Classification (EGFC) framework for semi-supervised disturbance detection and classification combined with a hybrid Hodrick-Prescott and Discrete-Fourier-Transform attribute-extraction method applied over a landmark window of voltage waveforms. Disturbances such as spikes, notching, harmonics, and oscillatory transient are considered. Different from other monitoring systems, which require offline training of models based on a limited amount of data and occurrences, the proposed online data-stream-based EGFC method is able to learn disturbance patterns autonomously from never-ending data streams by adapting the parameters and structure of a fuzzy rule base on the fly. Moreover, the fuzzy model obtained is linguistically interpretable, which improves model acceptability. We show encouraging classification results.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Fuzzy Systems, FUZZ 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169323
DOIs
StatePublished - Jul 2020
Externally publishedYes
Event2020 IEEE International Conference on Fuzzy Systems, FUZZ 2020 - Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameIEEE International Conference on Fuzzy Systems
Volume2020-July
ISSN (Print)1098-7584

Conference

Conference2020 IEEE International Conference on Fuzzy Systems, FUZZ 2020
Country/TerritoryUnited Kingdom
CityGlasgow
Period19/07/2024/07/20

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