The binary particle swarm optimization algorithm has shortcomings, such as slow convergence and the fact that it is easy for it to fall into the local optimal solution. Here a fault location method based on immune binary particle swarm optimization algorithm is proposed. First, the information processing mechanism of the immune system is applied to improve the particle swarm algorithm, and memory cell units are built to store high-quality antibodies during population evolution to avoid population degradation after antibody population updating. Secondly, the mechanism of antibody concentration regulation and immune selection are introduced to maintain the diversity of the antibody population, strengthen the global search ability of the algorithm and prevent premature algorithm. Finally, the improved algorithm is applied to the fault location of a distribution network by constructing an affinity evaluation function. The simulation results show that the algorithm based on immune binary particle swarm optimization can effectively improve the convergence speed and fault location accuracy, and has good fault tolerance in the case of fault information distortion. This work is supported by Science and Technology Project of the Headquarter of State Grid Corporation of China (No. 521710180008) “Immune Mechanism and Model Research for Self-cure Oriented Intelligent Distribution Network”.