基于粒子群的差分花朵授粉算法的无功优化
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摘要
电力系统无功优化,是确保电力系统安全、经济运行的一个有效处理手段,其主要目的是为了改善电压质量和减少电力线路的有功网损。电力系统无功优化问题包含对控制变量和状态变量的调节,是一个复杂的、非线性混合整数规划问题。针对于传统粒子群算法收敛速度和收敛精度方面的缺陷与不足,文章提出基于粒子群的差分变异花朵授粉算法(DFPA-PSO)。该算法融入花朵授粉算法中的全局搜索和局部搜索过程和差分算法中的变异操作。拓宽粒子搜索区域的同时,还增加粒子的多样性。将该算法应用于IEEE-14节点的标准测试电力系统中,综合考虑有功网损、电压偏移和电压稳定裕度三目标优化模型,将实验结果与其他算法进行相比较,明显看出该算法的寻优能力强,收敛速度优于其他算法,有功网损也有所降低,鲁棒性好,从而证明了本算法的优越性。
Reactive power optimization of the power system is an effective way to keep the power system safe and economical. The main purpose of reactive power optimization is that it can improve the voltage quality and reduce the active power loss of the power system. The reactive power optimization problem of the power system is a complex and nonlinear problem and it should adjust the variables including control variables and state variables. This paper establishes a differential evolution flower pollination algorithm based on particle swarm optimization(DFPA-PSO) in view of the shortcomings of the accuracy of the traditional particle swarm optimization algorithm. The DFPA-PSO combines global search, local search and mutation operations with the flower pollination algorithm. Not only can it widen the search space of particles, but also it increases the diversity of the particles. The DFPA-PSO is applied to the IEEE-14 bus system, which takes into account of loss minimization, voltage level best target and maximum static voltage stability margin. Compared with other algorithms, the results show that DFPA-PSO has stronger global searching ability, faster convergence rate,better robustness and the active power loss is also reduced, thus proving the superiority of DFPA-PSO.
Reactive power optimization of the power system is an effective way to keep the power system safe and economical. The main purpose of reactive power optimization is that it can improve the voltage quality and reduce the active power loss of the power system. The reactive power optimization problem of the power system is a complex and nonlinear problem and it should adjust the variables including control variables and state variables. This paper establishes a differential evolution flower pollination algorithm based on particle swarm optimization(DFPA-PSO) in view of the shortcomings of the accuracy of the traditional particle swarm optimization algorithm. The DFPA-PSO combines global search, local search and mutation operations with the flower pollination algorithm. Not only can it widen the search space of particles, but also it increases the diversity of the particles. The DFPA-PSO is applied to the IEEE-14 bus system, which takes into account of loss minimization, voltage level best target and maximum static voltage stability margin. Compared with other algorithms, the results show that DFPA-PSO has stronger global searching ability, faster convergence rate,better robustness and the active power loss is also reduced, thus proving the superiority of DFPA-PSO.
引文
[1]张武军,叶剑锋,梁伟杰等.基于改进遗传算法的多目标无功优化[J].电网技术,2004,28(11):67-71.Zhang W Y,Liang W L,et al.Multi-objective Reactive Power Optimization Based on Improved Genetic Algorithm[J].Power System Technology,2004,28(11):67-71.
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[10]马立新,孙进,彭华坤.多目标差分进化算法的电力系统无功优化[J].控制工程,2013,20(5):954-956.Ma L X,Sun J,Peng H K.Multi-Objective Differential Evolution Algorithm For Reactive Power Optimization[J].Control Engineering of China,2013,20(5):954-956.
[11]欧阳海滨,高立群,孔祥勇.随机变异差分进化算法[J].东北大学学报:自然学报,2013, 34(3):330-334.Ou Y H B,Gao L Qun,Kong X Y.Random Mutation Diffenrential Evolution Algorithm[J]Joumal of Northeastern University(Natural Science),2013, 34(3):330-334.
[12]马立新,王守征,吕新慧等.电力系统无功优化的反向优化差分进化算法[J].控制工程,2010, 17(6):803-806.Ma L X,Wang S Z,LV X H,et al.Opposition-based diffential evolution for reactive power optimization[J].Control Engineering,2010,17(6):803-806.
[13]李如琦,李芝荣,王维志等.基于差分策略的多目标电力系统无功优化[J].电网技术,2012,36(12):170-175.Li R Q,Li Z R,Wang W Z.Multi Objective Reactive Power Optimization Based on Difference Strategy[J].Power System Technology,2012,36(12):170-175.
[14] Shi Y,Gao H,Wu D.An improved different evolution algorithm with novel mutation stratery[C]//IEEE Symposium on Differential Evolution.2014:1-8.
[15] Walton S,Hassan O,Morgan K,et al.Modified cuckoo search:a new gradient free optimization algorithm[J].Chaps,Solitons&Fractals,201144(9):710-718.
[16]冯士刚,艾芊.带精英策略的快速非支配排序遗传算法在多目标无功优化中的应用[J].电工技术学报,2008, 22(12):146-151.Feng S G,Ai Q.Application of Fast and Elitist Non-Dominated Sorting Generic Algorithm in Multi-Objective Reactive Power Optimization,Transactions of China Electrotechnical Society,2008,22(12):146-151.
[2]马立新,单冠华,屈娜娜.基于改进粒子群算法的电力系统无功优化[J].电力科学与工程,2008,24(1):1-4.Ma L X,Wang S Z,LV X H,et al.Opposition Based Differential Evolution for Reactive Power Optimization[J].Control Engineering,2010,17(6):803-806.
[3]马玲,于青,刘刚,王泽黎.基于量子差分进化算法的电力系统无功优化[J].电力系统保护与控制,2013, 41(17):39-43.Ma L,Yu Q,Liu G,Wang Z L.Power system reactive optimization based on quantum DE algorithm.[J].Power System Protection and Control,2013, 41(17):39-43.
[4]王秀云,赵宇,马万明,王岩松,李书金.改进粒子群算法在无功优化中的应用[J].电测与仪表,2015,52(15):108-112.Wang X Y,Zhao Y,Ma W M,Wang Y S,Li S J.Application of improved particle swarm algorithm in reactive power optinization[J].Electrical Measurement&Instrument,2015,52(15):108-112.
[5] Mohammad Yunus Ali,Kaamran Raahemifar. Reactive Power optimization Based on Hybrid Particle Swarm Optimization Algorithm[C].25th IEEE Canada Conference on Electrical and Computer Engineering(CCECE),Canad,2012.
[6] Yang X S.Flower pollination algorithm for global optimizationp[C]//Proc of International Conference on Unconventional Coputing and Natural Computation.Berlin:Springer,2012:240-249.
[7]李亚男,张粒子,杨以涵.考虑电压约束裕度的无功优化及其内电解法[J].中国电机工程学报,2001,21(9):1-4.Li Y N,Zhang L Z,Yang Y H.Reactive power Optimization under voltage Constraints margin[J].Proceeding of the CSEE,2001,21(9):1-4.
[8] Lihong He,Nan Dynamic Constrained Optimization PSO Algorithm[C].Guilin(China),2009 Chinese Control and Conference on Natural Competation(ICNC2-008),Jinan,2008.
[9]肖辉辉,万常选,段艳明.一种改进的新型元启发式花朵授粉算法[J].计算机应用研究,2016, 33(1):127-131.Xiao H H,Wang C X,Duan Y M.Improved novel metaheuristic flower pollination algorithm[J]Application Research of Computer,2016, 33(1):127-131.
[10]马立新,孙进,彭华坤.多目标差分进化算法的电力系统无功优化[J].控制工程,2013,20(5):954-956.Ma L X,Sun J,Peng H K.Multi-Objective Differential Evolution Algorithm For Reactive Power Optimization[J].Control Engineering of China,2013,20(5):954-956.
[11]欧阳海滨,高立群,孔祥勇.随机变异差分进化算法[J].东北大学学报:自然学报,2013, 34(3):330-334.Ou Y H B,Gao L Qun,Kong X Y.Random Mutation Diffenrential Evolution Algorithm[J]Joumal of Northeastern University(Natural Science),2013, 34(3):330-334.
[12]马立新,王守征,吕新慧等.电力系统无功优化的反向优化差分进化算法[J].控制工程,2010, 17(6):803-806.Ma L X,Wang S Z,LV X H,et al.Opposition-based diffential evolution for reactive power optimization[J].Control Engineering,2010,17(6):803-806.
[13]李如琦,李芝荣,王维志等.基于差分策略的多目标电力系统无功优化[J].电网技术,2012,36(12):170-175.Li R Q,Li Z R,Wang W Z.Multi Objective Reactive Power Optimization Based on Difference Strategy[J].Power System Technology,2012,36(12):170-175.
[14] Shi Y,Gao H,Wu D.An improved different evolution algorithm with novel mutation stratery[C]//IEEE Symposium on Differential Evolution.2014:1-8.
[15] Walton S,Hassan O,Morgan K,et al.Modified cuckoo search:a new gradient free optimization algorithm[J].Chaps,Solitons&Fractals,201144(9):710-718.
[16]冯士刚,艾芊.带精英策略的快速非支配排序遗传算法在多目标无功优化中的应用[J].电工技术学报,2008, 22(12):146-151.Feng S G,Ai Q.Application of Fast and Elitist Non-Dominated Sorting Generic Algorithm in Multi-Objective Reactive Power Optimization,Transactions of China Electrotechnical Society,2008,22(12):146-151.