Voltage Sag Source Location Using Artificial Neural Network

Abstract - Voltage sag is one of the most severe power quality disturbances to be dealt with by the industrial sector, as it can cause severe process disruptions and results in substantial economic loss. A short-term decrease in voltage lasting anywhere from milliseconds up to a few seconds is called voltage sag. The most severe voltage sags are caused by faults in the power system. Sag originating due to faults propagates through the system affecting loads connected far away from the sag source. Therefore, the accountability for the generation of disturbances on the system must be assessed and the sag sources must be analyzed and located. Locating sag source in a complicated power system network is a difficult task. Conventional methods for locating sag source needs measurement of sag voltage and current. This paper introduces an alternative method for voltage sag source location based on voltage information using Artificial Neural Network (ANN). The source is located considering the sag magnitude at the primary and secondary side of a transformer. The performance of the proposed method is validated using PSCAD /EMTDC on a model of a regional network including transmission and subtransmission levels. The set of measurements taken from the regional network during a one year sag survey is used as training data for ANN. The results show the good performance of the new method and its unique applicability in cases where only voltages are recorded, such as the sag survey presented.