Optimal wavelet based feature extraction and classification of power quality disturbances using random forest
| Autor: | Marija Markovska, Dimitar Taskovski |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
020209 energy Multiresolution analysis Feature extraction Pattern recognition 02 engineering and technology computer.software_genre Random forest Electric power system Wavelet 0202 electrical engineering electronic engineering information engineering Entropy (information theory) Power quality Data mining Artificial intelligence Radio frequency business computer Mathematics |
| Zdroj: | EUROCON |
| DOI: | 10.1109/eurocon.2017.8011232 |
| Popis: | The increasing number of polluting loads requires higher power quality (PQ) in the generation, transmission and distribution systems. In order to improve the power quality, the power disturbances should be monitored continuously. Power quality monitoring and analysis must be able to detect and classify the disturbances on the electrical system. A new method for optimal features selection and classification using a wavelet based random forest (RF) classifier is proposed in this paper. The classification results are compared with some previously published results, obtained from similar works. The experiments have shown that the proposed method has better classification accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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