Orthogonal Experimental Design Based Binary Optimization Without Iteration for Fault Section Diagnosis of Power Systems

Fault section diagnosis (FSD) is considerably indispensable for the continuous and reliable electricity supply. In general, the analytical model of FSD is solved by derivative-free intelligent metaheuristic algorithms. However, intelligent metaheuristic algorithms require long iterations in the computation process, which leads to a time-consuming diagnostic process and difficulties in computing the correct results within a given computational resource. In addition, the stochastic nature of their evolutionary mechanism will lead to unstable diagnosis results. To overcome this shortcoming, we propose a simple yet efficient binary optimization method base on orthogonal experimental design in this article. This method does not require iterations in the calculations and relies only on a small number of representative combinations in the orthogonal table, resulting in less computational time and stable diagnosis results. The proposed method can identify valuable data between two initial fixed points quickly and utilize them to achieve the optimal solution without any iteration. Simulation results on different complex fault cases of two power systems indicate that it requires fewer computational resources to diagnose faults correctly compared with other methods. Besides, its diagnosis results are stable, reliable, and not affected by the complexity of fault scenarios.