基于G3A算法的配电网故障定位研究
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摘要
随着遗传算法、蚁群算法、神经网络算法等人工智能技术的引入,配电网故障判断领域的研究进入了一个新的阶段,由于遗传算法和蚁群算法各自存在的缺陷快速准确地实现故障区段的判断始终是一件比较困难的事情。为此,本文尝试将遗传算法和蚂蚁算法融合的GAAA算法引入到配电网故障定位算法的研究中。
首先利用遗传算法的随机搜索、快速行、全局收敛产生故障区段的初始信息分布。然后,充分利用蚂蚁算法的并行性、正反馈机制以及求解效率高等特性,根据遗传算法迭代后的最优组合形成馈线初始故障信息素分布,将遗传算法求解结果转换的信息素值代入蚁群算法的求解步骤中。
本文利用树状24馈线的配电网络发生2处故障时的情况对算法进行了仿真,通过对仿真所得数据进行分析比较,证明了GAAA算法的有效性。本文提出了多电源多点故障的情况下故障网的分离方法,同时还提出了馈线网的分离,避免了误判情况的出现。同时对评价函数也进行了改进,使得多电源多点故障下的配电网故障判成为可能,在本文中还探讨并实践了用遗传算法结果产生的选择概率来转换蚂蚁算法信息素值的计算方法。较传统的人工智能算法相比较,本文GAAA算法的引入有效的提高了配电网故障定位的定位速度和定位效率,使得算法能够应用在多电源多点故障的网络中。
With the used of genetic algorithm, ant colony algorithm and neural network algorithms as well as the artificial intelligence technology, the research of the fault judgment of distribution network entered a new stage. Because of the limitation of the genetic algorithms and ant colony algorithm, it is still a difficult thing to determine the fault area fleetly and correctly. In order to solve the problem, we try to use the GAAA algorithm to determine the fault section of the distribution network, which integrates the advantage of the genetic algorithms and ant algorithm.
First , we use random search, fast line, and overall convergence to obtain the initial distribution massage of the fault sections, which is a genetic algorithm process. Then according to the iterative Optimal portfolio of the initial fault massage of the feeder, the massage value conversed by the genetic algorithms is then used to the solving of the problem in the ant algorithm process, by the advantage of its parallel and positive feedback mechanisms, as well as high efficiency.
In this paper,a tree of the distribution network of 24 feeders in which two failures are occurred is used to simulate the algorithm, the simulation dates are compared and analysis, and the effectiveness of the GAAA algorithm is proved. We present a method of failure determination of the distribution network under the condition of multi-power multi-point in this paper, and we also present a method of feeder network separation, to avoid the emergence of a misjudgment of the situation. At the same time, the evaluation function is also improved too, which makes multi-power multi-point distribution network fault-contracting possible. The method of the massage value of ant colony algorithm conversion is also discussed and practiced, using the selection probability produced by the genetic algorithm. Compared to the traditional artificial intelligence algorithm, GAAA algorithm improve the speed and efficiency of fault location of the distribution network, and it can be used in multi-power multi-point failure of the power network.
首先利用遗传算法的随机搜索、快速行、全局收敛产生故障区段的初始信息分布。然后,充分利用蚂蚁算法的并行性、正反馈机制以及求解效率高等特性,根据遗传算法迭代后的最优组合形成馈线初始故障信息素分布,将遗传算法求解结果转换的信息素值代入蚁群算法的求解步骤中。
本文利用树状24馈线的配电网络发生2处故障时的情况对算法进行了仿真,通过对仿真所得数据进行分析比较,证明了GAAA算法的有效性。本文提出了多电源多点故障的情况下故障网的分离方法,同时还提出了馈线网的分离,避免了误判情况的出现。同时对评价函数也进行了改进,使得多电源多点故障下的配电网故障判成为可能,在本文中还探讨并实践了用遗传算法结果产生的选择概率来转换蚂蚁算法信息素值的计算方法。较传统的人工智能算法相比较,本文GAAA算法的引入有效的提高了配电网故障定位的定位速度和定位效率,使得算法能够应用在多电源多点故障的网络中。
With the used of genetic algorithm, ant colony algorithm and neural network algorithms as well as the artificial intelligence technology, the research of the fault judgment of distribution network entered a new stage. Because of the limitation of the genetic algorithms and ant colony algorithm, it is still a difficult thing to determine the fault area fleetly and correctly. In order to solve the problem, we try to use the GAAA algorithm to determine the fault section of the distribution network, which integrates the advantage of the genetic algorithms and ant algorithm.
First , we use random search, fast line, and overall convergence to obtain the initial distribution massage of the fault sections, which is a genetic algorithm process. Then according to the iterative Optimal portfolio of the initial fault massage of the feeder, the massage value conversed by the genetic algorithms is then used to the solving of the problem in the ant algorithm process, by the advantage of its parallel and positive feedback mechanisms, as well as high efficiency.
In this paper,a tree of the distribution network of 24 feeders in which two failures are occurred is used to simulate the algorithm, the simulation dates are compared and analysis, and the effectiveness of the GAAA algorithm is proved. We present a method of failure determination of the distribution network under the condition of multi-power multi-point in this paper, and we also present a method of feeder network separation, to avoid the emergence of a misjudgment of the situation. At the same time, the evaluation function is also improved too, which makes multi-power multi-point distribution network fault-contracting possible. The method of the massage value of ant colony algorithm conversion is also discussed and practiced, using the selection probability produced by the genetic algorithm. Compared to the traditional artificial intelligence algorithm, GAAA algorithm improve the speed and efficiency of fault location of the distribution network, and it can be used in multi-power multi-point failure of the power network.
引文
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[8]刘伟,郭志忠.配电网故障区间判断的新型矩阵算法[J].电力系统自动化,2006,26(18):54-57.
[9]李静一,肖保明,周正贵.配电网故障区段诊断改进矩阵算法研究[J].现代电力,2004,21(1):50-54.
[10]刘耀湘,乐秀璠,顾欣欣.配电网故障区段判断和隔离的综合矩阵法[J].电力自动化设备,2006,26(3):38-40.
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[19]姜秋霞,王中杰.混合蚁群算法的研究及其应用[J].装备制造技术,2008,2(4):36-38.
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[30]林景栋,曹长修,张帮礼,吴剑林.基于拓扑识别的配电网故障定位算法[J].重庆大学学报,2001,24(5):51-54.
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[34]钱涛.粗糙集及神经网络在配电网故障诊断中的应用研究[南京理工大学硕士学位论文] .南京:南京理工大学,2006:1-8.
[35]张宏.配电网馈线故障区域定位的研究[福州大学硕士学位论文].福州:福州大学,2003:7-12.
[36]肖宏峰,谭冠政.基于遗传算法的混合蚁群算法[J].计算机工程与应用,2008, 44(16):42-45.
[37]杨波,许奎,张志斌.基于有向分层电网故障定位的改进算法[J].广西大学学报,2007,32(增刊):55-57
[38]吴薛红,郑梅,唐小波,黄建新.配电网故障区间定位的新型矩阵算法[J].南京师范大学学报,2003,3(2):54-57.
[39]刘会家,王峥,陈玮.配电网故障定位统一矩阵算法的改进[J].三峡大学学报,2002,24(4):308-310.
[40]张宏,郭宗仁.基于节点-支路关联矩阵的配电网故障区域定位算法[J].电力自动化设备,2004,24(1):27-29.
[41]刘会家,李宁.一种基于FTU的馈线故障定位优化算法[J].继电器,2004,32(10):40-43.
[42]管德赛.配网自动化系统馈线故障诊断及隔离[J].电气应用,2005,24(1):26-28.
[43]蒋冬初.遗传算法交叉算子的性能研究[J].益阳师专学报,2002,19(13):26-28.
[44]杨伟,刘娅林,吴军基.基于改进遗传算法的配电网故障诊断[J].长沙电力学院学报自然科学版,2005,20(2):25-30.
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[2]崔明.变电站综合自动化[M].北京:中国水利水电出版社,2005:196-197.
[3]王小平,曹立明.遗传算法-理论、应用与软件实现[M].西安:西安交通大学出版社,2002:30-31.
[4] DORIGO M,ANIEZZO V,OLORNI A. Ant System Optimization by a Clony of Cooperation Agents.IEEE Trans on System,Man and Cybernetics—Part B:Cybernetics,1996,26(1):29-41.
[5]刘建,倪建立.配电网自动化新技术[M] .中国水利水电出版社,2005,13(5):196-197.
[6]夏雨,姚月娥,刘志全,王启章.配电网故障定位和隔离的新统一矩阵算法[J].高电压技术,2005,28 (3):4-8.
[7]王虎军,邵玉槐,潘艳霞,武在前.配电网馈线自动化的模式比较及故障判定[J].太原理工大学学报,2007,35(4):473-476.
[8]刘伟,郭志忠.配电网故障区间判断的新型矩阵算法[J].电力系统自动化,2006,26(18):54-57.
[9]李静一,肖保明,周正贵.配电网故障区段诊断改进矩阵算法研究[J].现代电力,2004,21(1):50-54.
[10]刘耀湘,乐秀璠,顾欣欣.配电网故障区段判断和隔离的综合矩阵法[J].电力自动化设备,2006,26(3):38-40.
[11]张钊.配电网故障定位的通用矩阵算法[J].电力自动化备,2005,25(5):40-42.
[12]陈歆技,丁同奎,张钊.蚁群算法在配电网故障定位中的应用[J].电力系统自动化,2006,30(5):74-77.
[13]毕天姝,倪以信,吴复立,杨奇逊.基于新型神经网络的电网故障诊断方法[J].中国电机工程学报,2002,22(2):73-78.
[14]卫志弄,何桦,郑玉平.配电网故障区间定位的高级遗传算法[J].中国电机工程学报,2002,22(4):127-130.
[15]束洪春,孙向飞等.基于粗糙集理论的配电网故障诊断研究[J].广西电力,2001,21(10):73-77.
[16]孙维明,吕航.PETRI网和冗余纠错技术结合的配网故障区段定位新方法[J].中国电机工程学报,2004,24(10):16-67.
[17]劭学俭,田磊.基于GIS的专家系统在配电网故障定位中的应用[J].浙江电力,1998,17(4):12-15.
[18]廖志伟,孙雅明等.基于数据挖掘模型的配电网故障定位诊断[J].天津大学学报,2002,35(3):332-326.
[19]姜秋霞,王中杰.混合蚁群算法的研究及其应用[J].装备制造技术,2008,2(4):36-38.
[20]岳琪,宋文龙,陈立生.遗传算法与组合优化问题研究[J].信息技术,2004,28(1):53-54.
[21]张航,罗熊.蚁群算法的研究状况及研究展望[J].信息与控制,2004, 33(3):318-323.
[22] K K Kuan,Prot K Warwick.Real-time exper system for fault location on high voltage underground distribution cables.IEE Proc-c vol,1992,5(3):139.
[23] Song Y H,Wang G S,John A T.Distribution Network Recongfiguration for Loss Reduction Using Fussy Controlled Evolutionary Programming[J].IEE Procee- dings-Gener Transm Distrib,1997,144(4):345-350.
[24] Hadjicostis C,Verghese G C.Povwer system monitoring using Petri net embedding[J].IEE Proceedings Generation,Transmission and Distribution.2000, 147(5):299-303.
[25] D Srinivasan, A C liew,C S Chang,J S P Chen.Intelligent operation of distribution network.IEE Proc-Gener Transm Disrtib,1998,9(5):145.
[26] Sheble G B,Kristin Britting.Refined Genetic Algorithm Economic Dispatch Example[J].IEEE Trans on Power System,1995.(2)10(1):117-124.
[27] Wen fushuan,Chang C S.Probabilistic Approach for Fault-Section Estimation in Power System Based on a Refined-Genetic Algorithm[J].IEE Proceedings- Generation, Transmission and Distribution,1997,144(2):160-168.
[28]黄鲁新.配电网的现状与发展[J].广西电力,2000,3(27):03-08.
[29]杨继革.基于遗传算法的配电网故障定位研究[浙江大学硕士学位论文].杭州:浙江大学,2004:3-8.
[30]林景栋,曹长修,张帮礼,吴剑林.基于拓扑识别的配电网故障定位算法[J].重庆大学学报,2001,24(5):51-54.
[31]杜红卫,孙雅明,刘弘靖,董伟.基于遗传算法的配电网故障定位和隔离[J].电网技术,2005,23(5):23-26.
[32]陈碧云,韦化,钟志东.基于遗传算法的配电网故障定位系统[J].广西电力.2002年,4(25) :1-3.
[33]郭壮志,陈波,刘灿平,许奎,李剑峰.基于遗传算法的配电网故障定位[J].电网技术,2007,31(11):88-92.
[34]钱涛.粗糙集及神经网络在配电网故障诊断中的应用研究[南京理工大学硕士学位论文] .南京:南京理工大学,2006:1-8.
[35]张宏.配电网馈线故障区域定位的研究[福州大学硕士学位论文].福州:福州大学,2003:7-12.
[36]肖宏峰,谭冠政.基于遗传算法的混合蚁群算法[J].计算机工程与应用,2008, 44(16):42-45.
[37]杨波,许奎,张志斌.基于有向分层电网故障定位的改进算法[J].广西大学学报,2007,32(增刊):55-57
[38]吴薛红,郑梅,唐小波,黄建新.配电网故障区间定位的新型矩阵算法[J].南京师范大学学报,2003,3(2):54-57.
[39]刘会家,王峥,陈玮.配电网故障定位统一矩阵算法的改进[J].三峡大学学报,2002,24(4):308-310.
[40]张宏,郭宗仁.基于节点-支路关联矩阵的配电网故障区域定位算法[J].电力自动化设备,2004,24(1):27-29.
[41]刘会家,李宁.一种基于FTU的馈线故障定位优化算法[J].继电器,2004,32(10):40-43.
[42]管德赛.配网自动化系统馈线故障诊断及隔离[J].电气应用,2005,24(1):26-28.
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