Power quality disturbances classification using the 3-D space representation and PCA based neuro-fuzzy approach

In this paper a new approach for power quality (PQ) event detection and classification is proposed. This approach is based on an automatic four step algorithm. First the acquired voltage signals are represented in a 3-D space referential. Then principal component analysis is performed. In the third, features are extracted from the obtained eigenvalues of each disturbance waveforms. Finally a neuro-fuzzy based classifier automatically classifies the PQ disturbances. To show the effectiveness of the proposed method several case studies are presented. From the obtained results it is possible to confirm that the proposed approach can effectively classify different PQ disturbances.