An End-to-End Deep Learning Method for Voltage Sag Classification

Radovan Turović, Dinu Dragan, Gorana Gojić, Veljko B. Petrović, Dušan B. Gajić, Aleksandar M. Stanisavljević and Vladimir A. Katić
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Radovan Turović: Faculty of Technical Sciences, University of Novi Sad, 21460 Novi Sad, Serbia
Dinu Dragan: Faculty of Technical Sciences, University of Novi Sad, 21460 Novi Sad, Serbia
Gorana Gojić: Faculty of Technical Sciences, University of Novi Sad, 21460 Novi Sad, Serbia
Veljko B. Petrović: Faculty of Technical Sciences, University of Novi Sad, 21460 Novi Sad, Serbia
Dušan B. Gajić: Faculty of Technical Sciences, University of Novi Sad, 21460 Novi Sad, Serbia
Aleksandar M. Stanisavljević: Faculty of Technical Sciences, University of Novi Sad, 21460 Novi Sad, Serbia
Vladimir A. Katić: Faculty of Technical Sciences, University of Novi Sad, 21460 Novi Sad, Serbia

Energies, 2022, vol. 15, issue 8, 1-22

Abstract: 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.

Keywords: power quality; classification; neural networks; voltage sag; dataset (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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