基于灰狼优化算法的多机电力系统稳定器参数最优设计

发布时间：2018-07-14 13:47

【摘要】：灰狼优化(grey wolf optimizer,GWO)算法作为一种新的、高效的群体智能优化算法,可应用于电力系统优化问题。提出了采用GWO算法的多机电力系统稳定器参数优化设计方案。将传统超前-滞后型电力系统稳定器(PSS)的参数设计建模为基于特征值的二次性能目标优化问题,通过向左半复平面移动机电振荡特征值实现对不同运行状态下机电模态阻尼系数的最大化进行寻优。GWO算法具有对初始取值不敏感,优化效率较高和全局寻优性能好等特点,因此被用来迭代搜索最优PSS参数值。通过IEEE New England 39节点算例的特征值分析和非线性时域仿真,验证了基于GWO算法优化整定的电力系统PSS在各种系统运行状态下抑制系统机电振荡的有效性和鲁棒性,并通过与传统相位补偿方法设计的PSS阻尼性能对比,表明所提GWO算法优化PSS参数具有明显优越性。进一步的算法性能分析表明,GWO算法具有对初值不敏感和稳健性强等优点。
[Abstract]:As a new and efficient swarm intelligence optimization algorithm, the gray wolf optimization (grey wolf optimizer GWO algorithm can be applied to power system optimization problems. The optimal design scheme of multi-machine power system stabilizer parameters using GWO algorithm is presented. The parameter design of traditional lead-lag power system stabilizer (PSS) is modeled as a quadratic performance goal optimization problem based on eigenvalue. By moving the eigenvalue of electromechanical oscillation to the left half complex plane, the method of optimizing the maximum damping coefficient of electromechanical mode under different running conditions has the advantages of being insensitive to the initial value, high optimization efficiency and good global optimization performance. Therefore, it is used to iteratively search for the optimal PSS parameter value. Through the eigenvalue analysis and nonlinear time domain simulation of IEEE New England 39 bus example, the effectiveness and robustness of PSS optimized tuning based on GWO algorithm are verified. By comparing with the PSS damping performance designed by the traditional phase compensation method, it is shown that the GWO algorithm has obvious advantages in optimizing the PSS parameters. Further performance analysis shows that the GWO algorithm is insensitive to initial values and robust.
【作者单位】：强电磁工程与新技术国家重点实验室(华中科技大学);
【分类号】：TM712;TP18

【相似文献】