Detection Methodology for Multistage Voltage Sag Based on Residual Analysis

The monitoring of voltage sags is a crucial prerequisite for mitigating their harmful effects. With the energy transition in power systems and the integration of a large number of distributed generations, multi-stage voltage sags (MSVS) are becoming increasingly common, causing more severe and profound impacts on the grid and users. Currently, there is a lack of effective description and detection methods for MSVS, posing challenges to their statistics, management, and prevention. Due to the varying residual voltages and multiple transition moments of MSVS, traditional methods struggle to effectively detect them. To address these difficulties, this study proposed a novel detection method based on residual analysis, tailored for the special characteristics of MSVS. First, relevant terms for MSVS were introduced, providing a foundation for the monitoring and management of MSVS. Secondly, a detection algorithm based on residual analysis is proposed, achieving accurate identification of MSVS and precise detection of their mutation moments. The effectiveness and accuracy were validated with data from a 10kV distribution network. The proposed method lays the foundation for improving the power quality of the existing energy transition system.