Open source dataset generator for power quality disturbances with deep-learning reference classifiers

R. Machlev*, A. Chachkes, J. Belikov, Y. Beck, Y. Levron

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In recent years power quality monitoring tools are becoming a necessity, and many studies focus on detection and classification of Power Quality Disturbances (PQD)s. However, presently a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a benchmark. In this light, we propose here an open-source software which enables the creation of synthetic power quality disturbances, and is designed specifically for comparison of PQD classifiers. The software produces several types of standard disturbances from the literature, with varying repetitions and random parameters of the labeled disturbances, and includes two reference classifiers that are based on deep-learning techniques. Due to the good performance of these classifiers, we suggest that they can be used by the community as benchmarks for the development of new and better PQD classification algorithms. The developed code is available online, and is free to use.

Original languageEnglish
Article number107152
JournalElectric Power Systems Research
Volume195
DOIs
StatePublished - Jun 2021

Funding

FundersFunder number
Israel Science Foundation1227/18

    Keywords

    • Classification
    • Classifier
    • Deep-learning
    • Harmonic distortion
    • PQD
    • Power quality
    • Public dataset

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