RBF and SVM Neural Networks for Automated Power Quality Events Classification |
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| Przemysław Janik, Tadeusz Łobos |
- Abstract:
- This paper presents classification results of different power quality disturbances. SVM and RBF neural networks are considered as appropriate classifiers for power quality issues, however SVM networks show better performance. Simulation of disturbed signals by parametric equations enabled the assessment of signal parameters influence on classification rate. Positive results encouraged further research. Model of supply system suffering from sags was simulated. Independent from line length and sag duration the classifier was set to recognize different sag types. The idea of space phasor was applied to obtain distinctive patterns from three phase system. Wavelet transform was used to find the beginning of sags. Positive classification results were obtained.
- Download:
- IMEKO-TC4-2005-066.pdf
- DOI:
- -
- Event details
- IMEKO TC:
- TC4
- Event name:
- TC4 Workshop 2005
- Title:
10th IMEKO TC4 International Workshop on ADC Modelling and Testing - IWADC (together with XIVth IMEKO TC4 International Symposium on New Technologies in Measurement and Instrumentation)
- Place:
- Gdynia/Jurata, POLAND
- Time:
- 12 September 2005 - 15 September 2005