Abstract
An ANN-based classifier for voltage wave disturbance was developed. Voltage signals captured on the power transmission system of CHESF, Federal Power Utility, were processed in two steps: by wavelet transform and principal component analysis. The classification was carried out using a combination of six MLPs with different architectures: five representing the first to fifth-level details, and one representing the fifth-level approximation. Network combination was formed using the boosting algorithm which weights a model’s contribution by its performance rather than giving equal weight to all models. Experimental results with real data indicate that boosting is clearly an effective way to improve disturbance classification accuracy when compared with the simple average and the individual models.
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Lira, M.M.S. et al. (2007). Boosting Algorithm to Improve a Voltage Waveform Classifier Based on Artificial Neural Network. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_47
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DOI: https://doi.org/10.1007/978-3-540-74695-9_47
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