为解决现有粒子群改进策略无法帮助已陷入局部最优和过早收敛的粒子恢复寻优性能的问题,提出一种陷阱标记联合懒蚂蚁的自适应粒子群优化(adaptive particle swarm optimization based on trap label and lazy ant, TLLA-APSO)算法。陷...为解决现有粒子群改进策略无法帮助已陷入局部最优和过早收敛的粒子恢复寻优性能的问题,提出一种陷阱标记联合懒蚂蚁的自适应粒子群优化(adaptive particle swarm optimization based on trap label and lazy ant, TLLA-APSO)算法。陷阱标记策略为粒子群提供动态速度增量,使其摆脱最优解的束缚。利用懒蚂蚁寻优策略多样化粒子速度,提升种群多样性。通过惯性认知策略在速度更新中引入历史位置,增加粒子的路径多样性和提升粒子的探索性能,使粒子更有效地避免陷入新的局部最优。理论证明了引入历史位置的粒子群算法的收敛性。仿真实验结果表明,所提算法不仅能有效解决粒子群已陷入局部最优和过早收敛的问题,且与其他算法相比,具有较快的收敛速度和较高的寻优精度。展开更多
The stability problem of power grids has become increasingly serious in recent years as the size of novel power systems increases.In order to improve and ensure the stable operation of the novel power system,this stud...The stability problem of power grids has become increasingly serious in recent years as the size of novel power systems increases.In order to improve and ensure the stable operation of the novel power system,this study proposes an artificial emotional lazy Q-learning method,which combines artificial emotion,lazy learning,and reinforcement learning for static security and stability analysis of power systems.Moreover,this study compares the analysis results of the proposed method with those of the small disturbance method for a stand-alone power system and verifies that the proposed lazy Q-learning method is able to effectively screen useful data for learning,and improve the static security stability of the new type of power system more effectively than the traditional proportional-integral-differential control and Q-learning methods.展开更多
文摘为解决现有粒子群改进策略无法帮助已陷入局部最优和过早收敛的粒子恢复寻优性能的问题,提出一种陷阱标记联合懒蚂蚁的自适应粒子群优化(adaptive particle swarm optimization based on trap label and lazy ant, TLLA-APSO)算法。陷阱标记策略为粒子群提供动态速度增量,使其摆脱最优解的束缚。利用懒蚂蚁寻优策略多样化粒子速度,提升种群多样性。通过惯性认知策略在速度更新中引入历史位置,增加粒子的路径多样性和提升粒子的探索性能,使粒子更有效地避免陷入新的局部最优。理论证明了引入历史位置的粒子群算法的收敛性。仿真实验结果表明,所提算法不仅能有效解决粒子群已陷入局部最优和过早收敛的问题,且与其他算法相比,具有较快的收敛速度和较高的寻优精度。
基金the Technology Project of China Southern Power Grid Digital Grid Research Institute Corporation,Ltd.(670000KK52220003)the National Key R&D Program of China(2020YFB0906000).
文摘The stability problem of power grids has become increasingly serious in recent years as the size of novel power systems increases.In order to improve and ensure the stable operation of the novel power system,this study proposes an artificial emotional lazy Q-learning method,which combines artificial emotion,lazy learning,and reinforcement learning for static security and stability analysis of power systems.Moreover,this study compares the analysis results of the proposed method with those of the small disturbance method for a stand-alone power system and verifies that the proposed lazy Q-learning method is able to effectively screen useful data for learning,and improve the static security stability of the new type of power system more effectively than the traditional proportional-integral-differential control and Q-learning methods.
