基于速度自适应粒子群优化算法的配电网网架优化研究
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摘要
配电网网架优化是配电网络规划中的重点和难点工作,对其进行优化规划可大大提高整体配电网运行的可靠性、经济性和安全性。
针对配电网网架优化问题,本文选用年综合费用最小的单阶段模型作为目标函数。在建立以上配电网网架优化模型的基础上,本文在原始粒子群优化算法(PSO)中设置动态最大限制速度基础上,提出一种速度自适应粒子群优化算法。经过神经网络的测试表明,该算法在收敛速度和精度上都优于原始算法,并且参数选取灵活,容易实现。将改进算法应用于配电网网架优化规划中,并利用盲信息模糊评价理论进行满意度评价,得出该算法在实际网架优化中的实用性和优越性。
Distribution network structure optimization is the focus of distribution network planning and difficult work, to optimize the planning can greatly enhance the overall operation of distribution network reliability, economy and security.
For the distribution network structure optimization problem, this choice of the smallest annual comprehensive cost of single-stage model as the objective function. In the establishment of the above optimization model for distribution network structure, based on the article in the original particle swarm optimization (PSO) is set dynamically based on the maximum speed limit, proposed a rate of Adaptive particle swarm optimization algorithm. Through the neural network tests show that the algorithm convergence speed and accuracy are better than the original algorithm, and parameter select flexible and easy to implement. Will improve the algorithm is applied to optimize the distribution network structure planning, and use the theory of fuzzy evaluation of blind information satisfaction evaluation, obtained the algorithm actual truss optimization practicality and superiority.
针对配电网网架优化问题,本文选用年综合费用最小的单阶段模型作为目标函数。在建立以上配电网网架优化模型的基础上,本文在原始粒子群优化算法(PSO)中设置动态最大限制速度基础上,提出一种速度自适应粒子群优化算法。经过神经网络的测试表明,该算法在收敛速度和精度上都优于原始算法,并且参数选取灵活,容易实现。将改进算法应用于配电网网架优化规划中,并利用盲信息模糊评价理论进行满意度评价,得出该算法在实际网架优化中的实用性和优越性。
Distribution network structure optimization is the focus of distribution network planning and difficult work, to optimize the planning can greatly enhance the overall operation of distribution network reliability, economy and security.
For the distribution network structure optimization problem, this choice of the smallest annual comprehensive cost of single-stage model as the objective function. In the establishment of the above optimization model for distribution network structure, based on the article in the original particle swarm optimization (PSO) is set dynamically based on the maximum speed limit, proposed a rate of Adaptive particle swarm optimization algorithm. Through the neural network tests show that the algorithm convergence speed and accuracy are better than the original algorithm, and parameter select flexible and easy to implement. Will improve the algorithm is applied to optimize the distribution network structure planning, and use the theory of fuzzy evaluation of blind information satisfaction evaluation, obtained the algorithm actual truss optimization practicality and superiority.
引文
[1]金华征,程浩忠.电力市场下的电网灵活规划方法综述[J].电力系统及其自动化学报,2006.4,18(2):11—12.
[2]杨丽徙,贾德峰,任群,陈根永.数据融合技术在变电站选址中的应用[J].电力自动化设备,2003(8):57-59.
[3] Schilling MT,Leite da Silva AM,Billinton R,eta1.Bibliography on power system probabilisticanalysis (1962—88)[J].IEEE Trans on PowerSystems,1990,5(1):1—11.
[4] Gorenstin B G,Campodonico N M,Costa J P,eta1.Power system expansion planning underuncertainty[J].IEEE Trans on Power Systems,1993,8(1):l29一l36.
[5]张洪明,廖培鸿.输电网规划的灰色模型及算法[J].上海交通大学学报,1995,29(3):20—26.
[6]孙洪波.电力网络规划IN].重庆:重庆大学出版社,l996.
[7]高赐威,程浩忠,王旭.盲信息的模糊评价模型在电网规划中的应用[J].中国电机工程学报,2004,24(9):25.
[8] Sun Hongbo,Yu D C.A multipleobjective optimization model of transm ission enhancement planningfor independent transmission company (ITC)[A].In:Proceedings of the IEEE Power EngineeringSociety Summer Meeting[c].Seattle,USA:2000(4):2033—2038.
[9]刘开第,吴和琴,庞彦军,等.不确定性信息数学处理及应用[M].北京:科学出版社,1999
[10]程浩忠,朱海峰,王建民,等.基于盲数BM模型的电网灵活规划方法[J].上海交通大学学报,2003,37(9):l347一l350.
[11]高赐威,程浩忠,王旭.盲信息的模糊评价模型在电网规划中的应用[J].中国电机工程学报,2004,24(9):24—29.
[12]王锡凡.电力系统优化规划IN].北京:水利电力出版社,1990.
[13] Binato S,Pereira M V F,Grranville S.A new benders decomposition approach to solve power transm ission network design problems[J].IEEE Trans onPower Systems,2001,16(2):235—240.
[14] Bahiense L,Dliveira G C,Pereira M ,et a1.A mixedinteger disjunctive model for transmission networkexpansion[J].IEEE Trans on Power Systems,2001,l6(3):560—565.
[15] Wong W ,Chao H,Julian D,et a1.Transmissionplanning in a deregulated environment[c]∥Proceedings of 1 999 IEEE Transmission and Distribution Conference.New Orleans,USA:1999:35O一355.
[16] Lee K Y ,M anuspiya S,M yeonsong Choi,et a1.Network congestion assessm ent for short—termtransmission planning under deregulatedenvironment[c]∥Proceedings of 2001 IEEEPower Engineering Society Transmission andDistribution Conference.Columbus,USA :2001:l266一l271.
[17] Kandi M S,EI—Debeiky S M ,Hasanien N E.Ahybrid mathematical and rule—based system fortransm ission network planning in a deregulatedenvironment[c]∥Proceedings of the IEEE PowerEngineering Society Summer M eeting.Vancouver,Canada:2001:l45l—l456.
[18]颜伟,王丽娜.基于改进免疫遗传算法的配电网网架规划[J].重庆大学学报,2007,30(1):28—30.
[19] Kit Po W ong,Yin W a W ong Suzannah.Combinedgenetic algorithm/simulated annealing/fuzzy setapproach to short—term generation scheduling withtake—or—pay fuel contract[J].IEEE Trans onPower Systems,1996,11(1):128—136.
[20] Fukuyama Y ,Hsai—Dong Chiang.A parallel geneticalgorithm for generation expansion planning[J].IEEE Trans on Power Systems,1996,11(2):955—961.
[21]毛玉宾,王秀丽,王锡凡.多阶段输电网路最优规划的遗传算法[J].电力系统自动化,1998,22(12):l3一l5.
[22] Wen Fushuan.Chang C S.Transmission networkoptimal planning using the tabu search method[J].Electric Power Systems Research,1997,42(2):153一l63.
[23]王志刚,杨丽徙,陈根永.基于蚁群算法的配电网网架优化规划方法[J].电力系统及其自动化学报,2002,14(6):73—75.
[24]翟海保,程浩忠,陈春霖,等.基于改进蚁群算法的输电网络扩展规划[J].中国电力,2003,36(12):49—52.
[25]翟海保,程浩忠,吕干云,等.多阶段输电网络最优规划的并行蚁群算法[J].电力系统自动化,2004,28(20):37—42.
[26] Kennedy J,Eberhart R.Particle swarm optimization[C]//Proceedings of the 1995 IEEE International Conference on Neural Networks.Perth,Aystralia,1995:1942—1948.
[27]胡家声,郭创新,叶彬,等.离散粒子群优化算法在输电网络扩展规划中的应用[J].电力系统自动化,2004,28(20):31—36.
[28]周弛,高海兵,高亮,等.粒子群优化算法.计算机应用研究,2003年第12期
[29] Fukuyama Y.Fundamentals of Particle Swarm Techniques[A],Lee KY,El-Sharkawi M A , Modern Heuristic Optimization Techniques With Applications to Power Systems[C].IEEE Power Engineering Society,2002:45～51
[30] Fan Huiyuan.A modification to particle swarm optimization algorit hm[J].Eingeering Coputations,2002,19 (18):970-989
[31]飞思科技产品研发中心.神经网络理论及MATLANB7实现[M] .北京:电子工业出版社,2005
[32]李博.粒子群优化算法及其在神经网络中的应用[D].大连:大连理工大学,2005
[33]石博强,赵德祥,李海鹏,肖成勇.基于盲数理论的最优化方法与程序实现[J].北京科技大学学报,2007,29(5):523—524.
[34] Kennedy J , Eberhart R C . A discrete binary version of the particle swarm algorithm.Proceedings of the World Multiconference on Systemics,Cybernetics and Informatics [C].Piscataway, NJ: IEEE Service Center,1997:4104-4109
[35]龚劬.网络和图的最优化方法,北京:中国铁道出版社,1984
[36]陈章潮,唐德光.城市电网规划与改造.北京:中国电力出版社,1998
[37]程浩忠,张焰.电力网络规划的方法与应用[M].上海:上海科学技术出版社,2002
[38]高炜新,罗先觉.基于蚂蚁算法的配电网网络规划.中国电机工程学报,2004,24(9):110～114
[39]徐珍霞,顾洁.粒子群优化算法在配电网网架优化规划中的应用.继电器,2006,6(34):29～33
[40]蒋秀洁,乐良才.基于改进PSO算法的电力系统机组优化组合.三峡大学学报(自然科学版),2005,6(27):504～508
[41]袁晓辉,王乘,袁艳斌,等.一种求解机组组合问题的新型改进粒子群方法,电力系统自动化,2005,1(29):34～38
[42]胡家声,郭创新,曹一家.一种适合于电力系统机组组合问题的混合粒子群优化算法.中国电机工程学报,2004,4(24):24～28
[43]赵波,郭创新,张鹏翔,等.基于分布式协同粒子群优化算法的电力系统无功优化.中国电机工程学报,2005,21(25):1～7
[44]刘自发,葛少云,余贻鑫.基于混沌粒子群优化方法的电力系统无功最优潮流.电力系统自动化,2005,7(29):53～57
[45]张宪,苑津沙,杨薛明等.基于粒子群算法的配电网网架规划.华北电力大学学报,2006,33(3):14～17
[46]马清峰.基于粒子群优化神经网络的齿轮箱故障诊断研究[D].太原:中北大学,2006
[2]杨丽徙,贾德峰,任群,陈根永.数据融合技术在变电站选址中的应用[J].电力自动化设备,2003(8):57-59.
[3] Schilling MT,Leite da Silva AM,Billinton R,eta1.Bibliography on power system probabilisticanalysis (1962—88)[J].IEEE Trans on PowerSystems,1990,5(1):1—11.
[4] Gorenstin B G,Campodonico N M,Costa J P,eta1.Power system expansion planning underuncertainty[J].IEEE Trans on Power Systems,1993,8(1):l29一l36.
[5]张洪明,廖培鸿.输电网规划的灰色模型及算法[J].上海交通大学学报,1995,29(3):20—26.
[6]孙洪波.电力网络规划IN].重庆:重庆大学出版社,l996.
[7]高赐威,程浩忠,王旭.盲信息的模糊评价模型在电网规划中的应用[J].中国电机工程学报,2004,24(9):25.
[8] Sun Hongbo,Yu D C.A multipleobjective optimization model of transm ission enhancement planningfor independent transmission company (ITC)[A].In:Proceedings of the IEEE Power EngineeringSociety Summer Meeting[c].Seattle,USA:2000(4):2033—2038.
[9]刘开第,吴和琴,庞彦军,等.不确定性信息数学处理及应用[M].北京:科学出版社,1999
[10]程浩忠,朱海峰,王建民,等.基于盲数BM模型的电网灵活规划方法[J].上海交通大学学报,2003,37(9):l347一l350.
[11]高赐威,程浩忠,王旭.盲信息的模糊评价模型在电网规划中的应用[J].中国电机工程学报,2004,24(9):24—29.
[12]王锡凡.电力系统优化规划IN].北京:水利电力出版社,1990.
[13] Binato S,Pereira M V F,Grranville S.A new benders decomposition approach to solve power transm ission network design problems[J].IEEE Trans onPower Systems,2001,16(2):235—240.
[14] Bahiense L,Dliveira G C,Pereira M ,et a1.A mixedinteger disjunctive model for transmission networkexpansion[J].IEEE Trans on Power Systems,2001,l6(3):560—565.
[15] Wong W ,Chao H,Julian D,et a1.Transmissionplanning in a deregulated environment[c]∥Proceedings of 1 999 IEEE Transmission and Distribution Conference.New Orleans,USA:1999:35O一355.
[16] Lee K Y ,M anuspiya S,M yeonsong Choi,et a1.Network congestion assessm ent for short—termtransmission planning under deregulatedenvironment[c]∥Proceedings of 2001 IEEEPower Engineering Society Transmission andDistribution Conference.Columbus,USA :2001:l266一l271.
[17] Kandi M S,EI—Debeiky S M ,Hasanien N E.Ahybrid mathematical and rule—based system fortransm ission network planning in a deregulatedenvironment[c]∥Proceedings of the IEEE PowerEngineering Society Summer M eeting.Vancouver,Canada:2001:l45l—l456.
[18]颜伟,王丽娜.基于改进免疫遗传算法的配电网网架规划[J].重庆大学学报,2007,30(1):28—30.
[19] Kit Po W ong,Yin W a W ong Suzannah.Combinedgenetic algorithm/simulated annealing/fuzzy setapproach to short—term generation scheduling withtake—or—pay fuel contract[J].IEEE Trans onPower Systems,1996,11(1):128—136.
[20] Fukuyama Y ,Hsai—Dong Chiang.A parallel geneticalgorithm for generation expansion planning[J].IEEE Trans on Power Systems,1996,11(2):955—961.
[21]毛玉宾,王秀丽,王锡凡.多阶段输电网路最优规划的遗传算法[J].电力系统自动化,1998,22(12):l3一l5.
[22] Wen Fushuan.Chang C S.Transmission networkoptimal planning using the tabu search method[J].Electric Power Systems Research,1997,42(2):153一l63.
[23]王志刚,杨丽徙,陈根永.基于蚁群算法的配电网网架优化规划方法[J].电力系统及其自动化学报,2002,14(6):73—75.
[24]翟海保,程浩忠,陈春霖,等.基于改进蚁群算法的输电网络扩展规划[J].中国电力,2003,36(12):49—52.
[25]翟海保,程浩忠,吕干云,等.多阶段输电网络最优规划的并行蚁群算法[J].电力系统自动化,2004,28(20):37—42.
[26] Kennedy J,Eberhart R.Particle swarm optimization[C]//Proceedings of the 1995 IEEE International Conference on Neural Networks.Perth,Aystralia,1995:1942—1948.
[27]胡家声,郭创新,叶彬,等.离散粒子群优化算法在输电网络扩展规划中的应用[J].电力系统自动化,2004,28(20):31—36.
[28]周弛,高海兵,高亮,等.粒子群优化算法.计算机应用研究,2003年第12期
[29] Fukuyama Y.Fundamentals of Particle Swarm Techniques[A],Lee KY,El-Sharkawi M A , Modern Heuristic Optimization Techniques With Applications to Power Systems[C].IEEE Power Engineering Society,2002:45～51
[30] Fan Huiyuan.A modification to particle swarm optimization algorit hm[J].Eingeering Coputations,2002,19 (18):970-989
[31]飞思科技产品研发中心.神经网络理论及MATLANB7实现[M] .北京:电子工业出版社,2005
[32]李博.粒子群优化算法及其在神经网络中的应用[D].大连:大连理工大学,2005
[33]石博强,赵德祥,李海鹏,肖成勇.基于盲数理论的最优化方法与程序实现[J].北京科技大学学报,2007,29(5):523—524.
[34] Kennedy J , Eberhart R C . A discrete binary version of the particle swarm algorithm.Proceedings of the World Multiconference on Systemics,Cybernetics and Informatics [C].Piscataway, NJ: IEEE Service Center,1997:4104-4109
[35]龚劬.网络和图的最优化方法,北京:中国铁道出版社,1984
[36]陈章潮,唐德光.城市电网规划与改造.北京:中国电力出版社,1998
[37]程浩忠,张焰.电力网络规划的方法与应用[M].上海:上海科学技术出版社,2002
[38]高炜新,罗先觉.基于蚂蚁算法的配电网网络规划.中国电机工程学报,2004,24(9):110～114
[39]徐珍霞,顾洁.粒子群优化算法在配电网网架优化规划中的应用.继电器,2006,6(34):29～33
[40]蒋秀洁,乐良才.基于改进PSO算法的电力系统机组优化组合.三峡大学学报(自然科学版),2005,6(27):504～508
[41]袁晓辉,王乘,袁艳斌,等.一种求解机组组合问题的新型改进粒子群方法,电力系统自动化,2005,1(29):34～38
[42]胡家声,郭创新,曹一家.一种适合于电力系统机组组合问题的混合粒子群优化算法.中国电机工程学报,2004,4(24):24～28
[43]赵波,郭创新,张鹏翔,等.基于分布式协同粒子群优化算法的电力系统无功优化.中国电机工程学报,2005,21(25):1～7
[44]刘自发,葛少云,余贻鑫.基于混沌粒子群优化方法的电力系统无功最优潮流.电力系统自动化,2005,7(29):53～57
[45]张宪,苑津沙,杨薛明等.基于粒子群算法的配电网网架规划.华北电力大学学报,2006,33(3):14～17
[46]马清峰.基于粒子群优化神经网络的齿轮箱故障诊断研究[D].太原:中北大学,2006