基于改进萤火虫算法的多目标优化潮流仿真研究
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摘要
针对电力系统多目标优化潮流(MOOPF)问题,结合基于约束优先的帕累托占优关系、非劣排序和拥挤距离计算,提出了约束优先非劣排序的多目标萤火虫算法(CNSFA),并根据模糊数学中的模糊隶属度选取最优折衷解。通过对IEEE30节点测试系统进行电力系统多目标优化潮流仿真测试以及与对比算法的比较可以看出:该算法在求解多目标优化潮流问题时,得到了分布性均匀和收敛性较强的帕累托解。
In view of the multi-objective optimal power flow(MOOPF)problem of the power system,and in combination with the Pareto dominance relation,non-inferior sorting and crowding distance calculation based on constraint priority,a constrain-prior non-dominated sorting firefly algorithm(CNSFA)proposed,and the optimal compromise solution is selected according to the fuzzy membership degree in fuzzy mathematics.Through the implementation of the simulation test of the multi-objective optimal power flow on the IEEE30 node testing system and the comparison with the contrast algorithm,it can be seen that this algorithm obtains the Pareto solution with the uniform distribution and strong convergence when the multi-objective optimal power flow problem is solved.
In view of the multi-objective optimal power flow(MOOPF)problem of the power system,and in combination with the Pareto dominance relation,non-inferior sorting and crowding distance calculation based on constraint priority,a constrain-prior non-dominated sorting firefly algorithm(CNSFA)proposed,and the optimal compromise solution is selected according to the fuzzy membership degree in fuzzy mathematics.Through the implementation of the simulation test of the multi-objective optimal power flow on the IEEE30 node testing system and the comparison with the contrast algorithm,it can be seen that this algorithm obtains the Pareto solution with the uniform distribution and strong convergence when the multi-objective optimal power flow problem is solved.
引文
[1]Chen G,Liu L,Song P,et al.Chaotic improved PSO-based multi-objective optimization for minimization of power losses and L index in power systems[J].Energy Conversion and Management,2014,86(10):548-560.
[2]尚荣华,焦李成,胡朝旭,等.修正免疫克隆约束多目标优化算法[J].软件学报,2012,23(7):1773-1786.
[3]Ye C,Huang M.Multi-Objective Optimal Power Flow Considering Transient Stability Based on Parallel NSGA-II[J].IEEE Transactions on Power Systems,2015,30(2):857-866.
[4]曾冰,李明富,张翼.基于改进萤火虫算法的装配序列规划方法[J].计算机集成制造系统,2014,20(4):799-806.
[5]陈功贵,杜阳维,郭艳艳,等.水轮机调速系统抗负荷扰动的动态仿真研究[J].实验技术与管理,2015,32(6):97-101,105.
[6]张文盛,刘忠宝.基于Matlab仿真的数据降维实验设计[J].实验技术与管理,2016,33(9):119-121.
[7]Chen G,Liu L,Zhang Z,et al.Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints[J].Applied Soft Computing,2017,50:58-70.
[8]陈功贵,刘利兰,郭艳艳,等.基于改进引力搜索算法的无功优化仿真研究[J].实验技术与管理,2016,33(5):113-116,120.
[9]陈功贵,易兴庭,熊国江,等.基于改进萤火虫算法的电力系统优化潮流仿真研究[J].实验室研究与探索,2017,36(6):93-97.
[10]杨俊友,崔嘉,田艳丰,等.计及网损最小的含分散式风电场配电网多目标优化策略[J].电网技术,2015,39(8):2141-2147.
[11]谭兴国,王辉,张黎,等.微电网复合储能多目标优化配置方法及评价指标[J].电力系统自动化,2014,38(8):7-14.
[12]张安安,方玮,刘凡,等.基于超体积因子搜索技术的电压/无功协调控制[J].电力系统自动化,2013,37(10):65-71.
[13]郑金华,李珂,李密青,等.一种基于Hypervolume指标的自适应邻域多目标进化算法[J].计算机研究与发展,2012,49(2):312-326.
[2]尚荣华,焦李成,胡朝旭,等.修正免疫克隆约束多目标优化算法[J].软件学报,2012,23(7):1773-1786.
[3]Ye C,Huang M.Multi-Objective Optimal Power Flow Considering Transient Stability Based on Parallel NSGA-II[J].IEEE Transactions on Power Systems,2015,30(2):857-866.
[4]曾冰,李明富,张翼.基于改进萤火虫算法的装配序列规划方法[J].计算机集成制造系统,2014,20(4):799-806.
[5]陈功贵,杜阳维,郭艳艳,等.水轮机调速系统抗负荷扰动的动态仿真研究[J].实验技术与管理,2015,32(6):97-101,105.
[6]张文盛,刘忠宝.基于Matlab仿真的数据降维实验设计[J].实验技术与管理,2016,33(9):119-121.
[7]Chen G,Liu L,Zhang Z,et al.Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints[J].Applied Soft Computing,2017,50:58-70.
[8]陈功贵,刘利兰,郭艳艳,等.基于改进引力搜索算法的无功优化仿真研究[J].实验技术与管理,2016,33(5):113-116,120.
[9]陈功贵,易兴庭,熊国江,等.基于改进萤火虫算法的电力系统优化潮流仿真研究[J].实验室研究与探索,2017,36(6):93-97.
[10]杨俊友,崔嘉,田艳丰,等.计及网损最小的含分散式风电场配电网多目标优化策略[J].电网技术,2015,39(8):2141-2147.
[11]谭兴国,王辉,张黎,等.微电网复合储能多目标优化配置方法及评价指标[J].电力系统自动化,2014,38(8):7-14.
[12]张安安,方玮,刘凡,等.基于超体积因子搜索技术的电压/无功协调控制[J].电力系统自动化,2013,37(10):65-71.
[13]郑金华,李珂,李密青,等.一种基于Hypervolume指标的自适应邻域多目标进化算法[J].计算机研究与发展,2012,49(2):312-326.