Naslov (eng)

An end-to-end deep learning method for voltage sag classification

Autor

Turović, Radovan
Dragan, Dinu
Gojić, Gorana
Petrović, Veljko
Gajić, Dušan
Stanisavljević, Aleksandar
Katić, Vladimir

Opis (eng)

Power quality disturbances (PQD) have a negative impact on power quality-sensitive equipment, often resulting in great financial losses. To prevent these losses, besides detecting a PQD on time, it is important to classify it, so that appropriate recovery procedures are employed. The majority of research employs machine learning model PQD classifiers on manually extracted features from simulated or real-world signals. This paper presents an end-to-end approach that circumvents the manual feature extraction and uses signals generated from mathematical voltage sag type formulas. We developed a configurable voltage sag generator that was used to form training and validation datasets. Based on the synthetic three-phase voltage signals, we trained several end-to-end LSTM classifiers that classify voltage sags according to ABC classification. The best-performing model achieved an accuracy of over 90% in the real-world dataset.

Jezik

engleski

Datum

2022

Licenca

Ovo delo je licencirano pod uslovima licence
Creative Commons CC BY 4.0 - Creative Commons Autorstvo 4.0 International License.

http://creativecommons.org/licenses/by/4.0/legalcode

Deo kolekcije (1)

o:1610

Radovi saradnika Instituta za veštačku inteligenciju