摘要
为解决电力系统稳定器(power system stabilizer,PSS)参数设置困难及传统的智能优化算法在寻优的过程中容易陷入局部最优,导致收敛速度下降的问题,因此文中采用混合策略改进SSA算法优化PSS参数。首先采用tent混沌映射来优化初始种群,提高种群多样性;采用柯西变异、正余弦策略和反向学习策略(opposition-based learning,OBL)来提升收敛速度;然后对6种测试函数进行寻优实验,将改进的SSA与PSO、GWO、SSA、SSSA优化结果对比,验证改进的SSA算法具有更好的收敛速度和稳定性;最后,将改进的SSA算法应用在单机无穷大系统及四机二区域系统的PSS参数优化中,与其他算法优化结果比较,验证改进的SSA在PSS参数优化方面的鲁棒性更好,收敛速度更快。
To solve the difficulty in setting PSS parameters and the problem that traditional intelligent optimization algorithms are prone to fall into local optimum during the optimization process,which leads to the decrease of convergence rate,a hybrid strategy is used to improve the SSA algorithm to optimize PSS parameters.First,a tent chaotic map is used to optimize the initial population and enhance the diversity of the ethnic groups.Cauchy mutation,sine-cosine strategy,and opposition-based learning(OBL)improve the convergence rate.Then six test functions are optimized.The improved SSA is compared with PSO,GWO,SSA,and SSSA to verify that the improved SSA algorithm has better convergence speed and stability.Finally,the enhanced SSA algorithm is applied to the PSS parameter optimization of a single-machine infinite-bus system and a four-machine two-area system.Compared with other algorithms,it is verified that the improved SSA has better robustness and faster convergence in PSS parameter optimization.
作者
付江涛
杨毅强
宋弘
FU Jiangtao;YANG Yiqiang;SONG Hong(School of Automation and Information Engineering,Sichuan University of Science&Engineering,Zigong 643000,China;Artificial Intelligence Key Laboratory of Sichuan Province,Zigong 643000,China;Aba Teachers University,Aba 623002,China)
出处
《中国测试》
北大核心
2025年第5期170-179,共10页
China Measurement & Test
基金
人工智能四川重点实验室项目(2019RYY01)
四川省科技厅项目(2020YFG0178,2021YFG0313)
四川理工学院四川省院士(专家)工作站项目(2018YSGZZ04)。
关键词
电力系统稳定器
低频振荡
改进的麻雀算法
参数优化
power system stabilizer
low-frequency oscillation
improved sparrow algorithm
parameter optimizatio
