[Submitted on 17 Apr 2020]
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.
Submission history
From: Daniel Leite [view email]
[v1]
Fri, 17 Apr 2020 07:08:17 UTC (1,005 KB)
