This paper provides an application of the hybrid Grey Wolf Optimization and Particle Swarm Optimization (GWO-PSO) method to reach a solution to the optimal reactive power dispatch (ORPD) problem in the scope of electric power networks. PSO is a swarm based meta-heuristic optimization algorithm whose target is to seek the best solution to a problem by moving particles in a specific exploration field. On the other hand, GWO is a meta-heuristic optimization technique which is inspired by grey wolves. In this article, GWO is hybridized with a PSO method to improve the progress of the GWO. There are two objectives minimized in this research study to improve the electric power network performance. They are: 1) power losses in the transmission systems, and 2) the deviation of voltages at the load buses. The problem of ORPD has many restrictions on the networks which must be considered during the solution. The hybrid GWO-PSO is proven as an effective optimization technique when seeking the global best solution to an optimization problem. The success of the introduced hybrid technique is verified utilizing more than one standard IEEE test system. A valuation to the introduced technique is performed by comparing it with other optimization techniques stated through the literature. The simulation results confirm that the usage of the hybrid GWO-PSO techniques causes an observable improvement in a wide scale of the electric power networks behavior.

本文提供了混合灰狼优化和粒子群优化（GWO-PSO）方法的应用，以解决电力网络范围内的最优无功调度（ORPD）问题。PSO是一种基于群体的元启发式优化算法，其目标是通过在特定勘探领域中移动粒子来寻求问题的最佳解决方案。另一方面，GWO是元-启发式优化技术，灵感来自灰狼。在本文中，将GWO与PSO方法杂交以改善GWO的进度。在这项研究中，有两个目标被最小化以提高电力网络的性能。它们是：1）传输系统中的功率损耗，以及2）负载母线上的电压偏差。ORPD问题对网络有很多限制，在解决方案中必须考虑这些限制。在寻求针对优化问题的全球最佳解决方案时，混合GWO-PSO被证明是一种有效的优化技术。引入的混合技术的成功使用了多个标准IEEE测试系统进行了验证。通过将引入的技术与文献中提到的其他优化技术进行比较，可以对引入的技术进行评估。