A New Detecting Method For Voltage Sag Source

Recently, with rapid updating and highly automatization of technologies in electric equipments, Voltage sags as the most important dynamic power quality (PQ) problem has brought forth widely attentions. And customers also have stricter demands on P Q. To decrease the impacts of sags as much as possible, the principal task is to introduce fast, effective and accurate method for voltage sags source detection.This paper first introduces the definition, harms, source, and international standards of voltage sags. In various power quality problems, voltage sags are the worst dynamic power quality problem. With high-power impulsive and fluctuating loads accessing electrical network, voltage fluctuations can't be tolerated in some power supply networks. It is necessary for detection, diagnosis, formulating mitigating devices of power quality faults to detect the source of voltage sags. And research mitigating devices to decrease the effect of voltage sags on loads. Accurate detect the source of voltage sags is helpful to evaluate the zone electric distribution system, select reasonable treatment measures, become the basis for settling dispute between power department and customs, solve the power quality problems, formulate standards which should be abided by power department and customs. So this paper mostly studies the method for voltage sags source detection.A method to determine the origin of voltage sag disturbance is proposed in this paper. Its principle is to estimate the incremental impedance of the nondisturbance side by utilizing the voltage and current changes caused by the disturbance. The sign of the real part of the impedance can reveal if the disturbance is from upstream or downstream. If the sign of the real part of the impedance is positive, the source of sag is on upstream; if the sign of the real part of the impedance is negative, the source of sag is on downstream. In order to improve the estimation of the impedance, we propose to utilize multiple cycles of data by using the least-squares (LS) method and Hough transform. The results of simulation verify the validity of this method, and the method can be easily implemented.