A Novel Algorithm for Classification of Voltage Sag Causes Using Alienation Coefficient and Power Quality Index
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Abstract
This paper presents a novel algorithm for detecting and classifying voltage sags in power systems using the alienation coefficient and Power Quality Index (PQI). Voltage sags were generated in a custom laboratory setup, with voltage and current signals recorded via an adlink data acquisition card. The algorithm computes the alienation coefficients of voltage and current for sags caused by induction motor starting (IMS), line-to-ground (LG) faults, and resistive load induced sag (RLIS). The PQI, derived as the product of voltage and current alienation coefficients, exhibits distinct signatures for each sag cause. In addition to analyzing the number of peak points, the algorithm incorporates the sum of peak points at the start and end of events to improve the identification of the voltage sag source. The algorithm also accurately detects the start and end of each voltage sag. By evaluating the PQI-I peak patterns and PQI-II (Psum), it distinguishes between RLIS (more peaks in the first group), IMS sags (peaks in the first group with no secondary peaks), and LG faults (more peaks in the second group). This method enhances power quality monitoring by providing reliable and accurate fault diagnosis.
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