A LSTM-based Deep Learning Method with Application to Voltage Dip Classification
Paper i proceeding, 2018

In this paper, a deep learning (DL)-based method for automatic feature extraction and classification of voltage dips is proposed. The method consists of a dedicated architecture of Long Short-Term Memory (LSTM), which is a special type of Recurrent Neural Networks (RNNs). A total of 5982 three-phase one-cycle voltage dip RMS sequences, measured from several countries, has been used in our experiments. Our results have shown that the proposedmethod is able to classify the voltage dips from learned features in LSTM, with 93.40% classification accuracy on the test data set. The developed architecture is shown to be novel for feature learning and classification of voltage dips. Different from the conventional machine learning methods, the proposed method is able to learn dip features without requiring transition-event segmentation, selecting thresholds, and using expert rules or human expert knowledge, when a large amount of measurement data is available. This opens a new possibility of exploiting deep learning technology for power quality data analytics and classification.

deep learning

RNN

voltage dips

LSTM

smart grid

Artificial intelligence

power quality

Författare

Ebrahim Balouji

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Irene Yu-Hua Gu

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Math H. J. Bollen

Luleå tekniska universitet

Azam Bagheri

Luleå tekniska universitet

Mahmood Nazari

2018 18TH INTERNATIONAL CONFERENCE ON HARMONICS AND QUALITY OF POWER (ICHQP)

2164-0610 (ISSN)

18th IEEE International Conference on Harmonics and Quality of Power (ICHQP)
Ljubljana, Slovenia,

Ämneskategorier

Annan data- och informationsvetenskap

Språkteknologi (språkvetenskaplig databehandling)

Datorseende och robotik (autonoma system)

DOI

10.1109/ICHQP.2018.8378893

ISBN

9781538605172

Mer information

Senast uppdaterat

2018-12-19