Fault Location of Active Distribution Network Based on Differential Evolution Grey Wolf Optimization Algorithm

After distributed power generations are connected to the power grid, the original fault location algorithm is not applicable, and the intelligent algorithm may fall into the local optimal solution. To solve these problems, this paper adopt a differential evolution grey wolf optimizer (DE-GWO) to locate faults in active distribution networks. First, the algorithm introduces the tent chaotic mapping for the high quality of initial population generation. Next, the differential evolution (DE) process is used to generate a mutated population and select individuals with high fitness for subsequent calculation to improve the algorithm's global search. Finally, the proposed method is simulated on the IEEE33-bus active distribution network model. It is verified that the proposed DE-GWO algorithm is faster and more accurate than the original GWO algorithm under both single fault and multiple faults, which can achieve accurate positioning.