A voltage sag measurement method based on RMS sliding window difference operator and sampled sequence reconstruction

In modern power systems, the integration of renewable energy sources exacerbates voltage sag issues, which significantly impact power quality. This paper proposes a novel voltage sag measurement method that combines a root mean square (RMS) sliding window difference operator with sampled sequence reconstruction to address the limitations of traditional detection methods. Unlike conventional approaches, the proposed method accurately identifies the start and end times of voltage sag using a predefined threshold, thereby ensuring precise interval isolation for detailed analysis. Moreover, phase jump detection is enhanced through sampled sequence reconstruction. These enable the accurate extraction of voltage sag characteristics, including depth, duration, and phase jump. The simplicity and computational efficiency of the algorithm make it highly suitable for implementation in embedded systems. Comprehensive simulations and real-world measurements have been conducted to validate the effectiveness of the proposed method, demonstrating significant improvements in voltage sag detection accuracy and robustness against noise and other disturbances.