为提高电压质量和降低网络损耗,建立以配电网节点电压总偏移指数最小为目标函数的含风电配电网无功优化模型,通过对狼群优化算法的探狼游走策略和猛狼运动步长进行改进,得到改进狼群算法(Improved Wolf Pack Algorithm,IWPA),以增强算...为提高电压质量和降低网络损耗,建立以配电网节点电压总偏移指数最小为目标函数的含风电配电网无功优化模型,通过对狼群优化算法的探狼游走策略和猛狼运动步长进行改进,得到改进狼群算法(Improved Wolf Pack Algorithm,IWPA),以增强算法的优化性能;采用IWPA算法对模型进行求解,并利用灵敏度分析确定无功补偿位置。算例分析结果表明,对含风电配电网进行无功优化,能够降低电压波动和减小网络损耗,验证了改进方法的正确性和有效性。展开更多
为提高主动配电网(Active Distribution Network,ADN)重构的经济性和稳定性,以主动配电网有功网络损耗最小和系统电压偏移指数最低作为目标函数,建立了主动配电网优化重构模型,利用目标规划法构建适应度函数,将多目标优化问题转化为单...为提高主动配电网(Active Distribution Network,ADN)重构的经济性和稳定性,以主动配电网有功网络损耗最小和系统电压偏移指数最低作为目标函数,建立了主动配电网优化重构模型,利用目标规划法构建适应度函数,将多目标优化问题转化为单目标进行求解。采用差分进化算法和Tent混沌映射对灰狼优化算法进行改进,以提高算法的优化性能。运用改进差分灰狼优化算法(Improved Differential Grey Wolf Optimization,IDEGWO)对主动配电网优化重构模型进行求解,并与其他算法对比,算例分析结果表明,改进差分灰狼优化算法在迭代次数和收敛精度方面均优于其他算法,按照IDEGWO算法优化方案重构后的有功网络损耗和系统电压偏移指数分别为57.91 kW和0.7856,主动配电网系统重构后的经济性和稳定性均得到了显著提升,验证了模型的正确性及求解方法的优越性。展开更多
为了提高输电线路覆冰预测精度,采用差分进化算法对灰狼优化算法进行改进,形成差分灰狼算法,采用差分灰狼算法(Differential Evolution Grey Wolf Optimization,DEGWO)对最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM...为了提高输电线路覆冰预测精度,采用差分进化算法对灰狼优化算法进行改进,形成差分灰狼算法,采用差分灰狼算法(Differential Evolution Grey Wolf Optimization,DEGWO)对最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)进行优化,建立基于DEGWO-LSSVM的输电线路覆冰厚度预测模型。采用两组实际运行线路的覆冰增长数据进行算例分析,并与其他覆冰预测方法对比,结果表明,DEGWO-LSSVM模型的误差波动更小,预测精度更高,验证了文章所提覆冰预测模型的正确性和实用性。展开更多
The biointerface engineering of living cells by creating an abiotic shell has important implications for endowing cells with exogenous properties with improved cellular behavior,which then boosts the development of th...The biointerface engineering of living cells by creating an abiotic shell has important implications for endowing cells with exogenous properties with improved cellular behavior,which then boosts the development of the emerging field of living cell hybrid materials.Herein,we develop a way to perform active nanoencapsulation of single cell,which then endows the encapsulated cells with motion ability that they do not inherently possess.The emerging motion characteristics of the encapsulated cells could be self-regulated in terms of both the motion velocity and orbits by different proliferation modes.Accordingly,by taking advantage of the emergence of differentiated moving abilities,we achieve the self-sorting between mother cells and daughter cells in a proliferated Saccharomyces cerevisiae cell community.Therefore,it is anticipated that our highlighted study could not only serve as a new technique in the field of single-cell biology analysis and sorting such as in studying the aging process in Saccharomyces cerevisiae,but also open up opportunities to manipulate cell functionality by creating biohybrid materials to fill the gap between biological systems and engineering abiotic materials.展开更多
文摘为提高电压质量和降低网络损耗,建立以配电网节点电压总偏移指数最小为目标函数的含风电配电网无功优化模型,通过对狼群优化算法的探狼游走策略和猛狼运动步长进行改进,得到改进狼群算法(Improved Wolf Pack Algorithm,IWPA),以增强算法的优化性能;采用IWPA算法对模型进行求解,并利用灵敏度分析确定无功补偿位置。算例分析结果表明,对含风电配电网进行无功优化,能够降低电压波动和减小网络损耗,验证了改进方法的正确性和有效性。
文摘为提高主动配电网(Active Distribution Network,ADN)重构的经济性和稳定性,以主动配电网有功网络损耗最小和系统电压偏移指数最低作为目标函数,建立了主动配电网优化重构模型,利用目标规划法构建适应度函数,将多目标优化问题转化为单目标进行求解。采用差分进化算法和Tent混沌映射对灰狼优化算法进行改进,以提高算法的优化性能。运用改进差分灰狼优化算法(Improved Differential Grey Wolf Optimization,IDEGWO)对主动配电网优化重构模型进行求解,并与其他算法对比,算例分析结果表明,改进差分灰狼优化算法在迭代次数和收敛精度方面均优于其他算法,按照IDEGWO算法优化方案重构后的有功网络损耗和系统电压偏移指数分别为57.91 kW和0.7856,主动配电网系统重构后的经济性和稳定性均得到了显著提升,验证了模型的正确性及求解方法的优越性。
文摘为了提高输电线路覆冰预测精度,采用差分进化算法对灰狼优化算法进行改进,形成差分灰狼算法,采用差分灰狼算法(Differential Evolution Grey Wolf Optimization,DEGWO)对最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)进行优化,建立基于DEGWO-LSSVM的输电线路覆冰厚度预测模型。采用两组实际运行线路的覆冰增长数据进行算例分析,并与其他覆冰预测方法对比,结果表明,DEGWO-LSSVM模型的误差波动更小,预测精度更高,验证了文章所提覆冰预测模型的正确性和实用性。
基金supported by the National Natural Science Foundation of China (Grant Nos.22171058 and 21871069)the Fundamental Research Funds for the Central Universities (Grant No.HIT.OCEF.2021027)。
文摘The biointerface engineering of living cells by creating an abiotic shell has important implications for endowing cells with exogenous properties with improved cellular behavior,which then boosts the development of the emerging field of living cell hybrid materials.Herein,we develop a way to perform active nanoencapsulation of single cell,which then endows the encapsulated cells with motion ability that they do not inherently possess.The emerging motion characteristics of the encapsulated cells could be self-regulated in terms of both the motion velocity and orbits by different proliferation modes.Accordingly,by taking advantage of the emergence of differentiated moving abilities,we achieve the self-sorting between mother cells and daughter cells in a proliferated Saccharomyces cerevisiae cell community.Therefore,it is anticipated that our highlighted study could not only serve as a new technique in the field of single-cell biology analysis and sorting such as in studying the aging process in Saccharomyces cerevisiae,but also open up opportunities to manipulate cell functionality by creating biohybrid materials to fill the gap between biological systems and engineering abiotic materials.
