Secondary Fault Identification Method Based on the Extremely Low Frequency Current Measurement

In electrical distribution grids, a single-phase to ground fault (SPGF) can lead to voltage levels increasing, challenging insulation performance, and potentially causing a secondary fault. Detecting the secondary fault is difficult be cause its characteristics are often obscured by the first fault This paper presents a novel approach to address this issue. I identifies significant differences in the extremely low-frequency current (ELFC) between faulty and healthy lines, utilizing a high-precision ELFC sensor based on the magnetic modulation principle. The ELFC feature data is collected and processed using a multi-class classification model based on the suppor vector machine (SVM) and error-correcting output code (ECOC), enabling the diagnosis of secondary faults. The simulation and experiment results show that it can effectively identify faulty lines, including single-line and double-line, improving the safety and reliability of power system operations.