基于双鱼群算法的电力系统无功优化
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摘要
提出一种基于双鱼群算法的电力系统无功优化方法.该方法以基本人工鱼群算法为基础,增加具有捕食行为的凶猛鱼群,重新定义鱼群的行为方式和寻优过程,并引入逃离因子以扩大搜索空间.采用随机尝试次数变化的动态歩长以增强双鱼群算法的全局优化能力.运用线性加权α法,以系统有功网损最低及电压偏差水平最优为目标,通过罚函数的形式建立无功优化模型.在无功优化目标函数中引入动态惩罚系数以提高算法的自适应调节能力.选取基本人工鱼群算法、改进遗传算法及所提出的双鱼群算法分别对IEEE14节点系统进行仿真实验,结果显示双鱼群算法在计算精度、收敛稳定性等方面均有明显优势,更切合电力系统运行的实际.
A double fish-swarm algorithm is proposed to solve reactive power optimization problems in power systems.In this paper, ferocious fish-swarm with the foraging behavior is added in the basic artificial fish-swarm algorithm. The behavior and optimizing process of fish-swam are redefined. The escape-factor is introduced to expand the search space.For enhancing the global optimization ability of the algorithm, the dynamic step is used in the algorithm, which is changing with a try number. The objective function aims at the minimum of the active power loss and voltage deviation,and the reactive power optimization model is established by using the α method and penalty function. In the reactive power optimization objective function, the dynamic penalty coefficient is introduced to improve the application ability of the algorithm. Finally, the basic artificial fish-swarm algorithm, the improved genetic algorithm, and the proposed algorithm have been applied to IEEE 14-bus system for testing the effectiveness. The simulation results show that double fish-swarm algorithm has the best optimization effect in terms of computational accuracy and convergence stability, which is much more in line with the actual operation of the power system.
A double fish-swarm algorithm is proposed to solve reactive power optimization problems in power systems.In this paper, ferocious fish-swarm with the foraging behavior is added in the basic artificial fish-swarm algorithm. The behavior and optimizing process of fish-swam are redefined. The escape-factor is introduced to expand the search space.For enhancing the global optimization ability of the algorithm, the dynamic step is used in the algorithm, which is changing with a try number. The objective function aims at the minimum of the active power loss and voltage deviation,and the reactive power optimization model is established by using the α method and penalty function. In the reactive power optimization objective function, the dynamic penalty coefficient is introduced to improve the application ability of the algorithm. Finally, the basic artificial fish-swarm algorithm, the improved genetic algorithm, and the proposed algorithm have been applied to IEEE 14-bus system for testing the effectiveness. The simulation results show that double fish-swarm algorithm has the best optimization effect in terms of computational accuracy and convergence stability, which is much more in line with the actual operation of the power system.
引文
[1]侯煦光.电力系统最优规划[M].武汉:华中理工大学出版社,1991.(Hou X G.Optimal planning in power system[M].Wuhan:Huazhong University of Science and Technology Press,1991.)
[2]程浩忠,吴浩.电力系统无功与电压稳定性[M].北京:中国电力出版社,2004.(Cheng H Z,Wu H.Reactive power and voltage stability of power system[M].Beijing:China Electric Power Press,2004.)
[3]周冬旭,李晓明.面向电力市场的含风电机组配电网无功优化模型及其求解算法[J].电力自动化设备,2013,33(4):24-29.(Zhou D X,Li X M.Reactive power optimization model and its algorithms for distribution system with wind farm in electricity market[J].Electric Power Automation Equipment,2013,33(4):24-29.)
[4]李智欢,李银红,段献忠.无功优化多目标模型转换方法的等值线分析[J].电工技术学报,2012,27(6):153-160.(Li Z H,Li Y H,Duan X Z.Analysis of objective-converting methods in multi-objective reactive power optimization using contour lines[J].Trans of China Electrotechnical Society,2012,27(6):153-160.)
[5]刘文学,梁军,贠志皓,等.基于可信理论的多目标模糊机会约束无功优化[J].电工技术学报,2015,30(21):82-89.(Liu W X,Liang J,Yun Z H,et al.Multi-objective fuzzy chance constrained optimal reactive power flow based on credibility theory[J].Trans of China Electrotechnical Society,2015,30(21):82-89.)
[6]程杉,陈民铀,黄薏宸.含分布式发电的配电网多目标无功优化策略研究[J].电力系统保护与控制,2013,41(10):45-50.(Cheng S,Chen M Y,Hang Y C.Multi-objective reactive power optimization of distribution system penetrated with distributed generation[J].Power System Protection and Control,2013,41(10):45-50.)
[7]吕忠,周强,蔡雨昌.含分布式电源的DEIWO算法配电网无功优化[J].电力系统保护与控制,2015,43(4):69-73.(Lv Z,Zhou Q,Cai Y C.Reactive power optimization in distribution network with distributed generation on DEIWO algorithm[J].Power System Protection and Control,2015,43(4):69-73.)
[8]韩平平,张佳琪,张晓安.电力市场下含双馈电机风电场的电力系统无功优化[J].电网技术,2017,41(1):172-177.(Han P P,Zhang J Q,Zhang X A.Reactive power optimization for power system containing DFIG wind farm in electricity market environment[J].Power System Technology,2017,41(1):172-177.)
[9]程新功,厉吉文,曹立霞,等.基于电网分区的多目标分布式并行无功优化研究[J].中国电机工程学报,2013,33(22):12-18.(Cheng X G,Li J W,Cao L X,et al.Multi-objective distributed parallel reactive power optimization based on subarea division of the power systems[J].Proc of the CSEE,2013,33(22):12-18.)
[10]刘明波,谢敏,赵维兴.大电网最优潮流计算[M].北京:科学出版社,2010.(Liu M B,Xie M,Zhao W X.The optimal power flow calculation of large power grid[M].Beijing:Science Press,2010.)
[11]李玉龙,宗伟,秦立军.电力系统无功优化线性规划问题中线性步长的动态调整策略[J].电网技术,2006,30(18):40-44.(Li Y L,Zong W,Qin L J.A new strategy of dynamically adjusting step size in linear programming model for power system reactive power optimization[J].Power Systems Technology,2006,30(18):40-44.)
[12]Momoh J A,Adapa R,El-Hawary M E.A review of selected optimal power flow literature to 1993,I:Nonlinear and quadratic programming approaches[J].IEEE Trans on Power Systems,1999,14(1):96-104.
[13]刘一兵,吴文传,张伯明,等.基于混合整数二阶锥规划的三相有源配电网无功优化[J].电力系统自动化,2014,38(15):58-64.(Liu Y B,Wu W C,Zhang B M,et al.Reactive power optimization for three-phase distribution networks wirh distributed generators based on mixed integer second-order cone programming[J].Automation of Electric Power Systems,2014,38(15):58-64.)
[14]李佳,刘天琪,李兴源,等.改进粒子群-禁忌搜索算法在多目标无功优化中的应用[J].电力自动化设备,2014,34(8):71-77.(Li J,Liu T Q,Li X Y,et al.Application of improved particle swarm-tabu search algorithm in multi-objective reactive power optimization[J].Electric Power Automation Equipment,2014,34(8):71-77.)
[15]崔挺,孙元章,徐箭,等.基于改进小生境遗传算法的电力系统无功优化[J].中国电机工程学报,2011,31(19):43-50.(Cui T,Sun Y Z,Xu J,et al.Reactive power optimization of power system based on improved niche genetic algorithm[J].Proc of the CSEE,2011,31(19):43-50.)
[16]刘斌,姜囡,井元伟.基于模拟退火算法的电力系统输电阻塞成本控制[J].控制与决策,2010,25(8):1184-1188.(Liu B,Jiang N,Jing Y W.Cost control of transmission congestion management in electricity systems based on simulated annealing algorithm[J].Control and Decision,2010,25(8):1184-1188.)
[17]韩敏,何泳.基于高斯混沌变异和精英学习的自适应多目标粒子群算法[J].控制与决策,2016,31(8):1372-1378.(Han M,He Y.Adaptive multi-objective particle swarm optimization with Gaussian chaotic mutation and elite learning[J].Control and Decision,2016,31(8):1372-1378.)
[18]李晓磊.一种新型的智能优化算法—–人工鱼群算法[D].杭州:浙江大学信息科学与工程学院,2003.(Li X L.A new inteligent optimization method—Artificial fish swarm algorithm[D].Hangzhou:School of Information Science and Engineering,Zhejiang University,2003.)
[19]马建伟,张国立.人工鱼群神经网络在电力系统短期负荷预测中的应用[J].电网技术,2005,29(11):36-39.(Ma J W,Zhang G L.Application of artificial fish-swarm neural network inshort-tem load forecasting of power system[J].Power System Technology,2005,29(11):36-39.)
[20]聂宏展,王毕元,孙金红,等.基于混沌人工鱼群算法的输电网规划方法[J].电网技术,2010,34(12):109-113.(Nie H Z,Wang B Y,Sun J H,et al.Transmission network planning based on chaotic artificial fish swarm algorithm[J].Power System Technology,2010,34(12):109-113.)
[21]唐剑东,熊信银.基于人工鱼群算法的电力系统无功优化[J].继电器,2004,32(19):9-12.(Tang J D,Xiong X Y.Reactive power optimization of power system based on artificial fish-swarm algorithm[J].Relay,2004,32(19):9-12.)
[22]郑华,刘伟,张粒子,等.基于改进人工鱼群算法的电网可用传输能力计算[J].电网技术,2008,32(10):84-88.(Zheng H,Liu W,Zhang L Z,et al.Available transfer capability calculation based on modified artificial fish school algorithm[J].Power System Technology,2008,32(10):84-88.)
[2]程浩忠,吴浩.电力系统无功与电压稳定性[M].北京:中国电力出版社,2004.(Cheng H Z,Wu H.Reactive power and voltage stability of power system[M].Beijing:China Electric Power Press,2004.)
[3]周冬旭,李晓明.面向电力市场的含风电机组配电网无功优化模型及其求解算法[J].电力自动化设备,2013,33(4):24-29.(Zhou D X,Li X M.Reactive power optimization model and its algorithms for distribution system with wind farm in electricity market[J].Electric Power Automation Equipment,2013,33(4):24-29.)
[4]李智欢,李银红,段献忠.无功优化多目标模型转换方法的等值线分析[J].电工技术学报,2012,27(6):153-160.(Li Z H,Li Y H,Duan X Z.Analysis of objective-converting methods in multi-objective reactive power optimization using contour lines[J].Trans of China Electrotechnical Society,2012,27(6):153-160.)
[5]刘文学,梁军,贠志皓,等.基于可信理论的多目标模糊机会约束无功优化[J].电工技术学报,2015,30(21):82-89.(Liu W X,Liang J,Yun Z H,et al.Multi-objective fuzzy chance constrained optimal reactive power flow based on credibility theory[J].Trans of China Electrotechnical Society,2015,30(21):82-89.)
[6]程杉,陈民铀,黄薏宸.含分布式发电的配电网多目标无功优化策略研究[J].电力系统保护与控制,2013,41(10):45-50.(Cheng S,Chen M Y,Hang Y C.Multi-objective reactive power optimization of distribution system penetrated with distributed generation[J].Power System Protection and Control,2013,41(10):45-50.)
[7]吕忠,周强,蔡雨昌.含分布式电源的DEIWO算法配电网无功优化[J].电力系统保护与控制,2015,43(4):69-73.(Lv Z,Zhou Q,Cai Y C.Reactive power optimization in distribution network with distributed generation on DEIWO algorithm[J].Power System Protection and Control,2015,43(4):69-73.)
[8]韩平平,张佳琪,张晓安.电力市场下含双馈电机风电场的电力系统无功优化[J].电网技术,2017,41(1):172-177.(Han P P,Zhang J Q,Zhang X A.Reactive power optimization for power system containing DFIG wind farm in electricity market environment[J].Power System Technology,2017,41(1):172-177.)
[9]程新功,厉吉文,曹立霞,等.基于电网分区的多目标分布式并行无功优化研究[J].中国电机工程学报,2013,33(22):12-18.(Cheng X G,Li J W,Cao L X,et al.Multi-objective distributed parallel reactive power optimization based on subarea division of the power systems[J].Proc of the CSEE,2013,33(22):12-18.)
[10]刘明波,谢敏,赵维兴.大电网最优潮流计算[M].北京:科学出版社,2010.(Liu M B,Xie M,Zhao W X.The optimal power flow calculation of large power grid[M].Beijing:Science Press,2010.)
[11]李玉龙,宗伟,秦立军.电力系统无功优化线性规划问题中线性步长的动态调整策略[J].电网技术,2006,30(18):40-44.(Li Y L,Zong W,Qin L J.A new strategy of dynamically adjusting step size in linear programming model for power system reactive power optimization[J].Power Systems Technology,2006,30(18):40-44.)
[12]Momoh J A,Adapa R,El-Hawary M E.A review of selected optimal power flow literature to 1993,I:Nonlinear and quadratic programming approaches[J].IEEE Trans on Power Systems,1999,14(1):96-104.
[13]刘一兵,吴文传,张伯明,等.基于混合整数二阶锥规划的三相有源配电网无功优化[J].电力系统自动化,2014,38(15):58-64.(Liu Y B,Wu W C,Zhang B M,et al.Reactive power optimization for three-phase distribution networks wirh distributed generators based on mixed integer second-order cone programming[J].Automation of Electric Power Systems,2014,38(15):58-64.)
[14]李佳,刘天琪,李兴源,等.改进粒子群-禁忌搜索算法在多目标无功优化中的应用[J].电力自动化设备,2014,34(8):71-77.(Li J,Liu T Q,Li X Y,et al.Application of improved particle swarm-tabu search algorithm in multi-objective reactive power optimization[J].Electric Power Automation Equipment,2014,34(8):71-77.)
[15]崔挺,孙元章,徐箭,等.基于改进小生境遗传算法的电力系统无功优化[J].中国电机工程学报,2011,31(19):43-50.(Cui T,Sun Y Z,Xu J,et al.Reactive power optimization of power system based on improved niche genetic algorithm[J].Proc of the CSEE,2011,31(19):43-50.)
[16]刘斌,姜囡,井元伟.基于模拟退火算法的电力系统输电阻塞成本控制[J].控制与决策,2010,25(8):1184-1188.(Liu B,Jiang N,Jing Y W.Cost control of transmission congestion management in electricity systems based on simulated annealing algorithm[J].Control and Decision,2010,25(8):1184-1188.)
[17]韩敏,何泳.基于高斯混沌变异和精英学习的自适应多目标粒子群算法[J].控制与决策,2016,31(8):1372-1378.(Han M,He Y.Adaptive multi-objective particle swarm optimization with Gaussian chaotic mutation and elite learning[J].Control and Decision,2016,31(8):1372-1378.)
[18]李晓磊.一种新型的智能优化算法—–人工鱼群算法[D].杭州:浙江大学信息科学与工程学院,2003.(Li X L.A new inteligent optimization method—Artificial fish swarm algorithm[D].Hangzhou:School of Information Science and Engineering,Zhejiang University,2003.)
[19]马建伟,张国立.人工鱼群神经网络在电力系统短期负荷预测中的应用[J].电网技术,2005,29(11):36-39.(Ma J W,Zhang G L.Application of artificial fish-swarm neural network inshort-tem load forecasting of power system[J].Power System Technology,2005,29(11):36-39.)
[20]聂宏展,王毕元,孙金红,等.基于混沌人工鱼群算法的输电网规划方法[J].电网技术,2010,34(12):109-113.(Nie H Z,Wang B Y,Sun J H,et al.Transmission network planning based on chaotic artificial fish swarm algorithm[J].Power System Technology,2010,34(12):109-113.)
[21]唐剑东,熊信银.基于人工鱼群算法的电力系统无功优化[J].继电器,2004,32(19):9-12.(Tang J D,Xiong X Y.Reactive power optimization of power system based on artificial fish-swarm algorithm[J].Relay,2004,32(19):9-12.)
[22]郑华,刘伟,张粒子,等.基于改进人工鱼群算法的电网可用传输能力计算[J].电网技术,2008,32(10):84-88.(Zheng H,Liu W,Zhang L Z,et al.Available transfer capability calculation based on modified artificial fish school algorithm[J].Power System Technology,2008,32(10):84-88.)