With the development of DC (direct current) distribution networks and the large-scale integration of distributed energy storage and photovoltaics into the distribution network, the structure of the distribution network has undergone revolutionary changes. After a short circuit fault occurs in the DC distribution network, the short circuit voltage drops sharply, the short circuit current rises rapidly, and the stability of the power system operation is disrupted. To address this issue, a model for inter pole and single pole short circuit faults in DC systems was proposed. Firstly, by sampling voltage data at both ends of the DC line, the voltage equation was written, and the transition resistance was eliminated. Then, a fitness function was constructed, and the adaptive optimization red fox algorithm with faster convergence speed and higher positioning accuracy was used to calculate the distance from the fault point to the protection installation site for fault location in the DC distribution network. Based on the red fox algorithm, combined with the isolation forest algorithm to remove abnormal data, the algorithm performance and accuracy were improved by improving adjustable feedback factors and introducing genetic crossover operators. When the sampling frequency is low, the accuracy of fault localization is improved through adaptive interpolation. Simulation verification was conducted in Simulink, and the results show that the method has strong resistance to transition resistance, small positioning error, is not affected by system parameters, and can effectively reduce the impact of low sampling frequency on fault localization, and has good robustness.