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自适应选择进化算法的多目标无功优化方法

, PP. 71-78

Keywords: 无功优化,多目标,多种进化算法,自适应选择,帕累托最优

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Abstract:

基于帕累托最优概念的多目标进化算法在电力系统无功优化领域已有广泛应用,但目前通过某种单一算法求解的方式由于进化算子的唯一性,难以保证进化过程不同寻优阶段的普适性和鲁棒性,因此提出一种基于多种进化算法自适应选择的多目标无功优化方法。通过分析已有多目标进化算法的特征,考虑协调性与互补性,建立包含4类算法的备选池;在进化过程不同阶段根据寻优性能自适应地确定备选算法的使用比例,从而综合多种算法的性能优势,提高整体寻优效率。以IEEE30节点标准系统的多目标无功优化为算例,从帕累托前沿、外部解及C指标等方面与已有单一算法的优化结果进行比较,表明所提新方法在整个进化过程中都显示出了更优的收敛特性。

References

[1]  Metropolis N,Rosenbluth A W,Rosenbluth M N,et al.Equation of state calculations by fast computing machines[J].The Journal of Chemical Physics,1953,21(6):1087-1092.
[2]  Liang C H,Chung C Y,Wong K P,et al.Parallel optimal reactive power flow based on cooperative co-evolutionary differential evolution and power system decomposition[J].IEEE Transactions on Power Systems,2007,22(1):249-257.
[3]  张伯明,陈寿孙,严正.高等电力网络分析[M].2版.北京:清华大学出版社,2007:325-328. Zhang Boming,Chen Shousun,Yan Zheng.Advanced power network analysis[M].2nd Edition.Beijing:Tsinghua University Press,2007:325-328(in Chinese).
[4]  舒隽,张粒子,刘易,等.电力市场下日无功计划优化模型和算法的研究[J].中国电机工程学报,2005,25(13):80-85. Shu Jun,Zhang Lizi,Liu Yi,et al.Study on the model and algorithm of daily reactive power scheduling in electricity market[J].Proceedings of the CSEE,2005,25(13):80-85(in Chinese).
[5]  熊虎岗,程浩忠,李宏仲.基于免疫算法的多目标无功优化[J].中国电机工程学报,2006,26(11):102-108. Xiong Hugang,Cheng Haozhong,Li Hongzhong.Multi-objective reactive power optimization based on immune algorithm[J].Proceedings of the CSEE,2006,26(11):102-108(in Chinese).
[6]  EL-Dib A A,Youssef H K M,EL-Metwally M M,et al.Optimum VAR sizing and allocation using particle swarm optimization[J].Electric Power Systems Research,2007,77(8):965-972.
[7]  刘佳,李丹,高立群,等.多目标无功优化的向量评价自适应粒子群算法[J].中国电机工程学报,2008,28(31):22-28. Liu Jia,Li Dan,Gao Liqun,et al.Vector evaluated adaptive particle swarm optimization algorithm for multi-objective reactive power optimization[J].Proceedings of the CSEE,2008,28(31):22-28(in Chinese).
[8]  Goldberg D E.Genetic algorithms in search,optimization,and machine learning reading[M].Boston:Addison-Wesley,1989:67-87.
[9]  李智欢,段献忠.多目标进化算法求解无功优化问题的对比分析[J].中国电机工程学报,2010,30(10):57-65. Li Zhihuan,Duan Xianzhong.Comparison and analysis of multiobjective evolutionary algorithm for reactive power optimization[J].Proceedings of the CSEE,2010,30(10):57-65(in Chinese).
[10]  Abido M A,Bakhashwain J M.Optimal VAR dispatch using a multiobjective evolutionary algorithm[J].International Journal of Electrical Power & Energy Systems,2005,27(1):13-20.
[11]  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,2007,22(12):146-151. Feng Shigang,Ai Qian.Application of fast and elitist non-dominated sorting generic algorithm in multi-objective reactive power optimization[J].Transactions of China Electrotechnical Society,2007,22(12):146-151(in Chinese).
[12]  Jeyadevi S,Baskar S,Babulal C K,et al.Solving multiobjective optimal reactive power dispatch using modified NSGA-II[J].International Journal of Electrical Power & Energy Systems,2011,33(2):219-228.
[13]  Li Z H,Li Y H,Duan X Z.Non-dominated sorting genetic algorithm-II for robust multi-objective optimal reactive power dispatch[J].IET Generation,Transmission & Distribution,2010,4(9):1000-1008.
[14]  Li Z H,Li Y H,Duan X Z.Multiobjective optimal reactive power flow using elitist nondominated sorting genetic algorithm:comparison and improvement[J].Journal of Electrical Engineering & Technology,2010,5(1):70-78.
[15]  Varadarajan M,Sworup K S.Solving multi-objective optimal power flow using differential evolution[J].IET Generation,Transmission & Distribution,2008,2(5):720-730.
[16]  Service T C.A no free lunch theorem for multi-objective optimization[J].Information Processing Letters,2010,110(21):917-923.
[17]  Vrugt J A,Robinson B A,Hyman J M.Self-adaptive multimethod search for global optimization in real-parameter spaces[J].IEEE Transactions on Evolutionary Computation,2009,13(2):243-259.
[18]  Muelas S,LaTorre A,Pena J.A new methodology for the automatic creation of adaptive hybrid algorithms[J].Intelligent Data Analysis,2012,16(1):3-23.
[19]  Vrugt J A,Robinson B A.Improved evolutionary optimization from genetically adaptive multimethod search[J].Proceedings of the National Academy of Sciences of the United States of America,2007,104(3):708-711.
[20]  Fonseca C M,Fleming P J.Multiobjective optimization and multiple constraint handling with evolutionary algorithms-Part I:A unified formulation[J].IEEE Transactions on Systems,Man and Cybernetics,Part A:Systems and Humans,1998,28(1):26-37.
[21]  Deb K,Pratap A,Agarwal S,et al.A fast and elitist multiobjective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[22]  Coello C C A.Evolutionary multi-objective optimization:some current research trends and topics that remain to be explored[J].Frontiers of Computer Science in China,2009,3(1):18-30.
[23]  Tsai S,Sun T,Liu C,et al.An improved multi-objective particle swarm optimizer for multi-objective problems[J].Expert Systems with Applications,2010,37(8):5872-5886.
[24]  Coello C C A,Pulido G T,Lechuga M S.Handling multiple objectives with particle swarm optimization[J].IEEE Transactions on Evolutionary Computation,2004,8(3):256-279.
[25]  Ali M,Siarry P,Pant M.An efficient differential evolution based algorithm for solving multi-objective optimization problems[J].European Journal of Operational Research,2012,217(2):404-416.
[26]  Das S,Suganthan P N.Differential evolution:a survey of the state-of-the-art[J].IEEE Transactions on Evolutionary Computation,2011,15(1):4-31.
[27]  Dasgupta S,Das S,Biswas A,et al.On stability and convergence of the population-dynamics in differential evolution[J].AI Communications,2009,22(1):1-20.
[28]  Haario H,Saksman E,Tamminen J.An adaptive Metropolis algorithm[J].Bernoulli,2001,7(2):223-242.

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