Pattern recognition method for identifying smart grid power quality disturbance

Pattern recognition of power quality (PQ) disturbances in electrical power distribution system especially in smart grids has developed into crucial topic for system equipment and end-user. Methodically analyzing the PQ disturbances can develop and maintain smart grids effectiveness. This study presents signal processing method Hilbert Huang Transform and computational intelligence methods such as Support Vector Machines, C4.5 Decision tree for automatic detection and classification of voltage sag in power grid. In this study based on experimental studies, Hilbert Huang based pattern recognition technique was used to investigate power signal to diagnose voltage sag in power grid. SVM and Decision Tree (C4.5) were operated and their achievements were matched for calculation error and CPU time. According to these analysis, decision tree algorithm produces the best solution.