Voltage Sags Detection and Identification Based on Phase-Shift and RBF Neural Network

Voltage sags are probably one of the most important power quality problems because of its impact on malfunctioning electrical equipment in industrial and commercial installations and high frequency. This fact highlights the need for an effective technique of detection, evaluation and classification of the sags problems. This paper proposed a voltage sags detection and identification method based phase-shift and RBF neural network. The voltage sag magnitude, duration and shape were extract out with the proposed phase-shift method, according to instantaneous virtual peak value. The proposed technique has good performance of real-time. Through a data dealing process of detecting outputs by the phase-shift method, a set of features is extracted for identification of voltage sags. Finally, a RBF network was developed for voltage sags classification according to the Cause. The proposed method is simple and reach 92% identification correct ratio even under noise. The results are useful for the diagnosis of the sags cause.