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Authors: Callum O’Donovan ; Cinzia Giannetti and Grazia Todeschini

Affiliation: College of Engineering, Swansea University, Fabian Way, Swansea, Wales, U.K.

Keyword(s): Classification, Feature Extraction, Power Quality Disturbance, Deep Learning, Convolutional Neural Network, LSTM, Recurrent Neural Network, Autoencoder.

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 disturba nces and reduce failures in real world power systems. (More)

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Paper citation in several formats:
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; ISSN 2184-433X, SciTePress, pages 373-380. DOI: 10.5220/0010347103730380

@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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - O’Donovan, C.
AU - Giannetti, C.
AU - Todeschini, G.
PY - 2021
SP - 373
EP - 380
DO - 10.5220/0010347103730380
PB - SciTePress