An alternative voltage sag source identification method utilizing radial basis function network

Power quality monitors (PQM) are required to be installed in a power supply network in order to assess power quality (PQ) disturbances such as voltage sags. However, with few PQMs installation, it is difficult to pinpoint the exact location of voltage sag. This paper proposes a new method for identifying the voltage sag source location by using the artificial neural network (ANN). Radial basis function networks are initially trained to estimate the unmonitored bus voltages during various sags caused by faults. Then voltage deviation of system buses is calculated to pinpoint voltage sag location. The validation of the proposed methodology is demonstrated by using an IEEE 30 Bus test system. The results shows that the proposed method can correctly locate the voltage sag source based on highest voltage deviation obtained through estimated unmonitored bus voltages.