基于改进微分进化算法的负荷模型参数辨识

为了提高电力系统中负荷模型的精确度,提出了一种改进的微分进化算法(IDE)以辨识负荷模型参数。采用不依赖于优化问题的控制参数自适应调整机制,同时考虑搜索速度和搜索精度,使算法摆脱后期易于陷入局部极值点的束缚,克服了微分进化算法参数调整困难的不足,提高了算法的寻优能力。将改进算法应用于静态负荷模型参数辨识的工程实例并与其他算法对比的结果表明,改进DE算法的全局搜索能力强,搜索精度高。

Identification of Parameters of Static Load Model by Improved Differential Evolution Algorithm

Differential evolution(DE) optimization is a computational technique. This paper introduces the DE algorithm, which is quite immune to local optimaztion and is fairly efficient in solving problems with complex hyperspace into the field of electrical load parameter identification. This application involves a suitable neighborhood distribution that assures the better global searching ability of the DE algorithm. The convergent efficiency and searching ability of the DE algorithm, IDE algorithm and PSO algorithm are compared. A conclusion is drawn that the improved differential evolution (IDE) algorithm is more efficient than DE and PSO in load parameter identification. An IDE algorithm is presented to identify parameters of power system load model. Despite its simplicity and high efficiency, the DE algorithm is prone to local optimal solution sometimes, and the parameters of differential evolution algorithm are hard to adopt dynamically.The IDE algorithm adopts adaptive control parameters according to swarms' distribution condition to improve its robust and global optimal searching capability. The proposed IDE algorithm is tested in a real load modeling case. Compared with other algorithms, the identification results show the improved differential evolution algorithm is a successful and feasible approach for load modeling,and the global convergences and convergence precision are better.