针对电力电子化配电网谐波源随机波动引发的治理效率与成本问题,提出融合谐波源随机表征与多目标粒子群-遗传算法(multi-objective particle swarm optimization-genetic algorithm,MOPSO-GA)的并联有源电力滤波器(shunt active power f...针对电力电子化配电网谐波源随机波动引发的治理效率与成本问题,提出融合谐波源随机表征与多目标粒子群-遗传算法(multi-objective particle swarm optimization-genetic algorithm,MOPSO-GA)的并联有源电力滤波器(shunt active power filter,SAPF)优化配置策略。基于中心极限定理,采用正态分布与均匀分布构建谐波幅值与相位的概率模型,结合MOPSO-GA算法实现多目标优化。仿真结果表明,在IEEE 18节点系统中仅配置3台SAPF即可将总谐波畸变率从19.88%降至3.32%,电压偏差从10.4%控制至4.5%,SAPF较传统MOPSO算法减少1台,总容量更经济,算法收敛速度与帕累托前沿分布性显著提升。并进一步通过RT-Lab半实物实验平台验证,在真实谐波源下将关键节点谐波电压畸变率从25.24%降至2.12%,该策略为复杂配电网谐波治理提供高效经济的解决方案。展开更多
Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search space.EAs have good running and mining perform...Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search space.EAs have good running and mining performance,but they still require huge computational resource and may miss many HUIs.Due to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded processors.Experiments show that the mining performance of PHUI-GA outperforms the existing EAs.When mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.展开更多
文摘针对电力电子化配电网谐波源随机波动引发的治理效率与成本问题,提出融合谐波源随机表征与多目标粒子群-遗传算法(multi-objective particle swarm optimization-genetic algorithm,MOPSO-GA)的并联有源电力滤波器(shunt active power filter,SAPF)优化配置策略。基于中心极限定理,采用正态分布与均匀分布构建谐波幅值与相位的概率模型,结合MOPSO-GA算法实现多目标优化。仿真结果表明,在IEEE 18节点系统中仅配置3台SAPF即可将总谐波畸变率从19.88%降至3.32%,电压偏差从10.4%控制至4.5%,SAPF较传统MOPSO算法减少1台,总容量更经济,算法收敛速度与帕累托前沿分布性显著提升。并进一步通过RT-Lab半实物实验平台验证,在真实谐波源下将关键节点谐波电压畸变率从25.24%降至2.12%,该策略为复杂配电网谐波治理提供高效经济的解决方案。
基金This work was supported by the National Natural Science Foundation of China(62073155,62002137,62106088,62206113)the High-End Foreign Expert Recruitment Plan(G2023144007L)the Fundamental Research Funds for the Central Universities(JUSRP221028).
文摘Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search space.EAs have good running and mining performance,but they still require huge computational resource and may miss many HUIs.Due to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded processors.Experiments show that the mining performance of PHUI-GA outperforms the existing EAs.When mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.
