Improving Disturbance Classification by Combining Multiple Artificial Neural Networks

An ANN-based automatic classifier for power system disturbance waveforms was developed. Actual voltage waveforms were applied in the training process. Signals are processed in two steps: i) decomposition through wavelet transformation up to the 5th decomposition level; ii) the resultant wavelet coefficients are processed via PCA, reducing the input space of the classifier to a much lower dimension. The classification is carried out using a combination of 3 MLPs with different architectures. The RPROP algorithm is applied for training the networks. Network combination was formed and the final decision of the classifier corresponds to the combination output with the highest value. The results showed to be quite promising for five disturbance types tested so far: sags, swells, harmonics, oscillatory transients and interruptions, as well as in the particular case of no disturbance.