A LSTM-based deep learning method with application to voltage dip classification
| Autor: | Math Bollen, Ebrahim Balouji, Mahmood Nazari, Irene Yu-Hua Gu, Azam Bagheri |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry 020209 energy Deep learning Feature extraction Pattern recognition 02 engineering and technology Set (abstract data type) Recurrent neural network 0202 electrical engineering electronic engineering information engineering Data analysis Segmentation Artificial intelligence business Feature learning Test data |
| Zdroj: | 2018 18th International Conference on Harmonics and Quality of Power (ICHQP). |
| DOI: | 10.1109/ichqp.2018.8378893 |
| Popis: | 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 proposed method 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. |
| Databáze: | OpenAIRE |
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