An intelligent optimization method of power system restoration path based on orthogonal genetic algorithm

Optimization of power system restoration path is a key issue to the system restoration following a significant disruption, such as the Northeast Blackout of 2003 in the United States and Canada. The restoration path optimization problem (RPOP) is to calculate the shortest restoration path between specified nodes, while subject to network security constraints. The RPOP is normally modeled as a large-scale mixed integer nonlinear programming, including both routing components and the nonlinear steady-state power flow equations. Intelligent algorithm is widely used to solve this complicated problem due to its excellent optimization capability, but existing research didn't concern about the generate method of initial population. In this paper, an orthogonal genetic algorithm is adopted to achieve the optimal solutions. The orthogonal array method is used to generate an initial population of genetic algorithm. This method has been proven to be optimal to select representative samples from all the possible combinations, due to the selected samples scatter uniformly over the feasible solution space. Finally, the IEEE standard test systems are used to examine the applicability of proposed method. Simulation results demonstrate that the proposed method is more efficient than traditional method.