Automatic identification of power quality events using a machine learning approach
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| Format: | Artículo científico |
| Language: | en |
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Universidad Tecnológica de Pereira
2019
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| _version_ | 1876449588019200000 |
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| author | A. F. Valencia-Duque |
| author_facet | A. F. Valencia-Duque |
| contents | Automatic identification of power quality events using a machine learning approach A. F. Valencia-Duque A. M. Álvarez Meza A. A. Orozco-Gutiérrez Ingeniería time frequency power quality domain features Machine learning In nowadays, Power Quality (PQ) events have been studied because they represent an essential aspect for the industries concerning the efficiency and the useful life of the elements connected to electrical systems. If the disturbances related to PQ events are classified (identified) fast and with reliable accuracy, the costs and losses generated would be reduced. In this paper, we present a machine learning-based approach to identify PQ events. Our proposal comprises the following stages: we employ a feature representation space based on time and frequency parameters. Besides, we include a supervised relevance analysis technique, called Relieff, to highlight the discriminant capability of the considered features. Then, we evaluate the success of classifying PQ events with different classifiers by adding different levels of noise under a cross-validation scheme. For concrete testing, a synthetic database based on the IEEE 1159 standard is generated, considering 3000 signals and ten classes (300 samples per class). Remarkably, obtained results show a suitable classification performance holding straightforward classifiers, e.g., quadratic and k-NN, in comparison to those state-of-the-art methodologies. 2019 artículo científico 0122-1701 https://www.redalyc.org/articulo.oa?id=84961237006 https://www.redalyc.org/journal/849/84961237006/ https://www.redalyc.org/journal/849/84961237006/html/ https://www.redalyc.org/journal/849/84961237006/84961237006.epub https://www.redalyc.org/journal/849/84961237006/movil en http://www.redalyc.org/revista.oa?id=849 Scientia Et Technica application/pdf Universidad Tecnológica de Pereira Scientia Et Technica (Colombia) Num.2 Vol.24 |
| format | Artículo científico |
| id | redalyc_84961237006 |
| institution | Redalyc |
| language | en |
| publishDate | 2019 |
| publisher | Universidad Tecnológica de Pereira |
| spellingShingle | Automatic identification of power quality events using a machine learning approach A. F. Valencia-Duque Ingeniería time frequency power quality domain features Machine learning Automatic identification of power quality events using a machine learning approach A. F. Valencia-Duque A. M. Álvarez Meza A. A. Orozco-Gutiérrez Ingeniería time frequency power quality domain features Machine learning In nowadays, Power Quality (PQ) events have been studied because they represent an essential aspect for the industries concerning the efficiency and the useful life of the elements connected to electrical systems. If the disturbances related to PQ events are classified (identified) fast and with reliable accuracy, the costs and losses generated would be reduced. In this paper, we present a machine learning-based approach to identify PQ events. Our proposal comprises the following stages: we employ a feature representation space based on time and frequency parameters. Besides, we include a supervised relevance analysis technique, called Relieff, to highlight the discriminant capability of the considered features. Then, we evaluate the success of classifying PQ events with different classifiers by adding different levels of noise under a cross-validation scheme. For concrete testing, a synthetic database based on the IEEE 1159 standard is generated, considering 3000 signals and ten classes (300 samples per class). Remarkably, obtained results show a suitable classification performance holding straightforward classifiers, e.g., quadratic and k-NN, in comparison to those state-of-the-art methodologies. 2019 artículo científico 0122-1701 https://www.redalyc.org/articulo.oa?id=84961237006 https://www.redalyc.org/journal/849/84961237006/ https://www.redalyc.org/journal/849/84961237006/html/ https://www.redalyc.org/journal/849/84961237006/84961237006.epub https://www.redalyc.org/journal/849/84961237006/movil en http://www.redalyc.org/revista.oa?id=849 Scientia Et Technica application/pdf Universidad Tecnológica de Pereira Scientia Et Technica (Colombia) Num.2 Vol.24 |
| title | Automatic identification of power quality events using a machine learning approach |
| topic | Ingeniería time frequency power quality domain features Machine learning |
| url | https://www.redalyc.org/articulo.oa?id=84961237006 https://www.redalyc.org/journal/849/84961237006/ https://www.redalyc.org/journal/849/84961237006/html/ https://www.redalyc.org/journal/849/84961237006/84961237006.epub https://www.redalyc.org/journal/849/84961237006/movil |