基于逐次优化改进遗传算法的特高压穿越无功规划

与超高压线路相比,特高压线路无功大量富余,会与下级电网形成很大的穿越无功,从而影响无功的分层控制,甚至威胁电力系统的安全稳定运行。常规的优化算法存在维数灾问题,即使是智能算法,也由于解空间维度大而寻优效率低下。对此,提出了一种基于逐次优化改进遗传算法,该方法利用逐次优化的思想,对传统遗传算法的寻优方式进行了改进,并将该算法应用于某实际区域大电网中求解无功规划问题。结果表明,该方法不仅有效降低了解空间的维度,且在保证算法效率的同时使寻优的效果得到较大的改善。

Penetration Reactive Power Planning Based on Modified Successive Optimization Genetic Algorithm

Compared with super-high voltage (SHV) transmission lines, ultra-high voltage (UHV) has plenty of reactive power. Therefore, a great amount of penetration reactive power exists in different voltage level grid, which puts great threats on the security and stability of power system. The traditional optimization method has the curse of dimensionality. Intelligent algorithm has lower search efficiency because the solutions of dimensionality are very large. To settle back these drawbacks, a modified successive optimization genetic algorithm (SGA) is applied to optimize reactive power planning of a certain regional power system. The numerical results show that the proposed method can not only reduce the solution of dimensionality effectively, but also improve the search efficiency.