Abstract:Grid fault diagnosis plays an important role in power system operation. In order to diagnose the fault area quickly and accurately after a fault occurs, this paper proposes an improved adaptive binary operator differential evolutionary algorithm (IABODE). The algorithm avoids the conversion process from floating-point number to binary number by directly encoding individuals in binary, and designs binary and pairwise transformation operators, and uses adaptive parameter adjustment to improve the search performance and population diversity of the algorithm.Through a typical four-substation system and a power grid fault case test of the improved power grid fault diagnosis model, the algorithm is compared and analyzed from multiple perspectives such as accuracy, rapidity and convergence, and the results show that it is superior to the other four popular meta-heuristic algorithms in solving the power grid fault problem.