Bird swarm algorithm(BSA), a novel bio-inspired algorithm, has good performance in solving numerical optimization problems. In this paper, a new improved bird swarm algorithm is conducted to solve unconstrained optimi...Bird swarm algorithm(BSA), a novel bio-inspired algorithm, has good performance in solving numerical optimization problems. In this paper, a new improved bird swarm algorithm is conducted to solve unconstrained optimization problems. To enhance the performance of BSA, handling boundary constraints are applied to fix the candidate solutions that are out of boundary or on the boundary in iterations, which can boost the diversity of the swarm to avoid the premature problem. On the other hand, we accelerate the foraging behavior by adjusting the cognitive and social components the sin cosine coefficients. Simulation results and comparison based on sixty benchmark functions demonstrate that the improved BSA has superior performance over the BSA in terms of almost all functions.展开更多
由于风电互联系统结构复杂并且具有随机性,传统控制器难以满足系统多运行方式下的阻尼控制效果,为提高含风电互联系统抑制低频振荡的能力,提出静止无功补偿器(static var compensator,SVC)附加双通道广域阻尼控制方法。首先建立附加双...由于风电互联系统结构复杂并且具有随机性,传统控制器难以满足系统多运行方式下的阻尼控制效果,为提高含风电互联系统抑制低频振荡的能力,提出静止无功补偿器(static var compensator,SVC)附加双通道广域阻尼控制方法。首先建立附加双通道控制器模型;其次基于频域子空间辨识与几何测度结合法设计最佳控制回路,实测方便,更有利于应用在复杂电网;最后采用基于多目标函数的改进型鸟群算法(improved bird swarm algorithm,IBSA)对控制器进行优化,确定控制参数。将上述研究方法通过含风电的两区四机系统进行仿真验证,结果表明接入设计控制器的系统阻尼大大提高,控制效果显著,能够快速抑制振荡,从而增强系统稳定性能。展开更多
基金Supported by the National Natural Science Foundation of China(11871383,71471140 and 11771058)
文摘Bird swarm algorithm(BSA), a novel bio-inspired algorithm, has good performance in solving numerical optimization problems. In this paper, a new improved bird swarm algorithm is conducted to solve unconstrained optimization problems. To enhance the performance of BSA, handling boundary constraints are applied to fix the candidate solutions that are out of boundary or on the boundary in iterations, which can boost the diversity of the swarm to avoid the premature problem. On the other hand, we accelerate the foraging behavior by adjusting the cognitive and social components the sin cosine coefficients. Simulation results and comparison based on sixty benchmark functions demonstrate that the improved BSA has superior performance over the BSA in terms of almost all functions.
