A Novel Deep Learning Power Quality Disturbance Classification Method using Autoencoders

Callum O’Donovan, Cinzia Giannetti, Grazia Todeschini

2021

Abstract

Automatic identification and classification of power quality disturbances (PQDs) is crucial for maintaining efficiency and safety of electrical systems and equipment condition. In recent years emerging deep learning techniques have shown potential in performing classification of PQDs. This paper proposes two novel deep learning models, called CNN(AE)-LSTM and CNN-LSTM(AE) that automatically distinguish between normal power system behaviour and three types of PQDs: voltage sags, voltage swells and interruptions. The CNN-LSTM(AE) model achieved the highest average classification accuracy with a 65:35 train-test split. The Adam optimiser and a learning rate of 0.001 were used for ten epochs with a batch size of 64. Both models are trained using real world data and outperform models found in literature. This work demonstrates the potential of deep learning in classifying PQDs and hence paves the way to effective implementation of AI-based automated quality monitoring to identify disturbances and reduce failures in real world power systems.

Paper Citation

in Harvard Style

O’Donovan C., Giannetti C. and Todeschini G. (2021). A Novel Deep Learning Power Quality Disturbance Classification Method using Autoencoders.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 373-380. DOI: 10.5220/0010347103730380

in Bibtex Style

@conference{icaart21,
author={Callum O’Donovan and Cinzia Giannetti and Grazia Todeschini},
title={A Novel Deep Learning Power Quality Disturbance Classification Method using Autoencoders},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={373-380},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010347103730380},
isbn={978-989-758-484-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - A Novel Deep Learning Power Quality Disturbance Classification Method using Autoencoders
SN - 978-989-758-484-8
AU - O’Donovan C.
AU - Giannetti C.
AU - Todeschini G.
PY - 2021
SP - 373
EP - 380
DO - 10.5220/0010347103730380