A Framework Based on Machine Learning for Analytics of Voltage Quality Disturbances
Artikel i vetenskaplig tidskrift, 2022

This paper proposes a machine-learning-based framework for voltage quality analytics, where the space phasor model (SPM) of the three-phase voltages before, during, and after the event is applied as input data. The framework proceeds along with three main steps: (a) event extraction, (b) event characterization, and (c) additional information extraction. During the first step, it utilizes a Gaussian-based anomaly detection (GAD) technique to extract the event data from the recording. Principal component analysis (PCA) is adopted during the second step, where it is shown that the principal components correspond to the semi-minor and semi-major axis of the ellipse formed by the SPM. During the third step, these characteristics are interpreted to extract additional information about the underlying cause of the event. The performance of the framework was verified through experiments conducted on datasets containing synthetic and measured power quality events. The results show that the combination of semi-major axis, semi-minor axis, and direction of the major axis forms a sufficient base to characterize, classify, and eventually extract additional information from recorded event data.

Power quality

Anomaly detection

Principal component analysis

Space phasor model

Machine learning

Författare

Azam Bagheri

AI & Future Technologies

Roger Alves De Oliveira

Luleå tekniska universitet

Math H.J. Bollen

Luleå tekniska universitet

Irene Yu-Hua Gu

Chalmers, Elektroteknik

Energies

1996-1073 (ISSN) 19961073 (eISSN)

Vol. 15 4 1283

Ämneskategorier

Annan data- och informationsvetenskap

Analytisk kemi

Datorseende och robotik (autonoma system)

DOI

10.3390/en15041283

Mer information

Senast uppdaterat

2022-04-05