Evolving fault diagnosis scheme for unbalanced distribution network using fast normalized cross-correlation technique

There has been a lack of a satisfactory solution for identifying and locating evolving faults in unbalanced distribution systems. The proposed approach is based on the cross-correlation technique as a key element for fault detection and location. Evolving faults, in this context, refer to two sequential faults that result in a change of fault phase. The captured QRS value reflects the occurrence of the second fault occurrence. In order to identify Evolving Faults, it makes use of the signal that is currently being monitored at any given point in the network. Typical system occurrences, such as a short circuit fault that grew into another short circuit fault, as well as cross-country faults, are simulated, and according to the suggested technique, they are accurately differentiated from one another. Using a real-time simulator, rigorous simulations are performed on the modified IEEE 240 bus distribution system. The results of these simulations reveal that they have the potential to uncover defects that are constantly changing. Regardless of the fault (location\resistance\inception angle), location of the monitored point, or sample frequency that is selected, the suggested approach is unaffected by any of these factors. In addition, the slime mold optimization approach is utilized in order to get the best monitoring points that accurately identify the bus in which the evolving fault has taken place.