Abstract
Distribution networks frequently experience cross-country failures (CCFs).This study presents research that demonstrates how Cross-Correlation may be used to extract distinctive features from faulty current signals, hence providing a threshold-based technique for fault identification. The most challenging aspect of dealing with HIF syndrome-related CCFs is recognizing and categorizing them. In this research, the complex, aperiodic, asymmetric, and nonlinear features of the signals generated by CCF with HIF syndrome were recovered using the Cross Correlation method. The low frequencies are filtered out using a high pass filter, leaving only the positive peak and two negative peaks in the vicinity as unique features of the correlogram. In this work, the proposed approach is put to the test on the modified imbalanced IEEE 240 Bus system. The suggested method is assessed through the lens of several different case studies, such as the switching of a capacitor bank, a reactor string, a load, a feeder, the effects of a power swing, nonlinear loads, lightly laden conditions, and measurement noise. The normal and faulty signals can be distinguished by their normalized QRS levels. By selecting the most appropriate sensors using the Jellyfish optimization method, we were also able to ascertain which bus housed CCHIF.