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Automatic power quality disturbance classification using wavelet, Support Vector Machine and Artificial Neural Network
| Content Provider | Semantic Scholar |
|---|---|
| Author | Vega, Valdomiro Kagan, Nelson Ordonez, Gabriel Duarte, Cesar |
| Copyright Year | 2009 |
| Abstract | Abstract— This paper considers two important classification algorithms for to classify several power quality disturbances. Artificial Neural Network (ANN) and support vector machine (SVM). The last one is a novel algorithm that has shown good performance in general patterns classification. Nevertheless, Multilayer Perceptron Artificial Neural Network (MLPANN) is the most popular and most widely used models in various applications. Both are used for classify some disturbances under survey as: low frequency disturbances (such as flicker and harmonics) and high frequency disturbances (such as transient and sags). Biorthogonal Wavelet Function is used as a base function for extract features of PQ disturbances. In addition, RMS value is used to characterize the magnitude of disturbances. |
| Starting Page | 1 |
| Ending Page | 4 |
| Page Count | 4 |
| File Format | PDF HTM / HTML |
| DOI | 10.1049/cp.2009.1123 |
| Alternate Webpage(s) | http://www.cired.net/publications/cired2009/pdfs/CIRED2009_1020_Paper.pdf |
| Alternate Webpage(s) | https://doi.org/10.1049/cp.2009.1123 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
