Abstract: The classical power grid fault diagnosis analytical model based on integer programming is of simple logic,strong interpretability,and strong practicality. However,it is constrained by the relatively high dimensionality of the models,making it challenging to employ precise algorithms for objective function solving. As a substitute,heuristic optimization algorithms like genetic algorithms are utilized,resulting in solutions with only approximate accuracy. Furthermore,each attempt at solving may yield inconsistent results,leading to an increase in misdiagnosis rates. Additionally,heuristic algorithms entail longer solving times, which are detrimental to fault identification based on diagnostic outcomes and equipment maintenance. To address these challenges,this study enhances traditional fault diagnosis models by linearizing the objective function,thereby reducing model complexity. This adjustment paves the way for the introduction of Gurobi, a strong commercial solver tailored to integer linear programming problems. Leveraging the characteristics of Gurobi heuristic algorithms and linear programming, we propose a solution approach for diagnosing models based on 0-1 integer linear programming. This approach overcomes the limitations of traditional heuristic optimization algorithms,which often get trapped in local optima,produce erroneous results,or exhibit slow solving speeds. Finally,through numerical examples,the effectiveness and superiority of the novel power grid fault diagnosis model are validated. The results demonstrate that compared to the traditional models that employ heuristic algorithms like genetic algorithms, simulated annealing, or particle swarm optimization for solving,the improved model significantly enhances both speed and accuracy in solving.