负荷冲击型大扰动下船舶综合电网暂态电压稳定性研究
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摘要
随着电力科学技术的发展及应用,船舶综合电力系统的规模不断扩大,各种拖动、监控、保护设备逐渐实现电气自动化,对电力的需求大量增加。同时船舶综合电网中的单台负荷容量也越来越大,冲击性负荷并网造成船舶综合电网电压骤降的情况时有发生,因此船舶综合电力系统暂态电压稳定性的研究受到了越来越多的重视。本文在总结前人研究成果的基础上,对船舶综合电网大扰动下暂态电压稳定性问题中的几个关键方面进行了深入的研究。
分析了船舶综合电力系统及大负荷冲击下的暂态电压失稳场景的特点,指出船舶综合电力系统暂态电压稳定性的研究具有其特殊性。然后基于上述特点对船舶综合电力系统进行了数学建模,运用电力系统暂态稳定分析中的时域仿真法对数学模型进行大扰动下的暂态电压稳定数值仿真。以某船舶综合电力系统为仿真算例证明了数学模型及数值积分方法的正确性。该内容可作为进一步研究暂态电压稳定性的基本方法与验证手段。
提出了一种判断船舶综合电力系统是否处于暂态电压失稳边界的时变特征分析方法。该方法首先回顾了小扰动分析中特征分析法的主要结论,结合船舶综合电力系统暂态过渡过程的综合数学模型,提出在每一时步将系统状态方程组按泰勒级数展开,获得在该时步的线性化Jacobian表达式并进行小扰动分析；通过求解出全部特征根、左右特征向量及定义重要状态变量与之对应特征向量的动态相关因子,给出船舶综合电力系统是否处于暂态电压失稳状态的理论判据。通过某船舶综合电力系统的仿真算例进行了验证,证明了上述方法的有效性。
提出了一种基于BP神经网络的船舶综合电网暂态电压稳定边界上的CUEP计算方法。该方法在电力系统稳定域边界理论基础上,建立系统受扰后各切除时刻状态及达到稳态后系统状态间的高维非线性映射关系；通过多次不同大扰动切除时刻的暂态稳定数值仿真,记录下多组受扰后各切除时刻状态变量及达到稳态后系统状态变量,将其分别作为输入与输出样本训练BP神经网络；估算持续故障轨迹与该故障模式主导下稳定流形的交界点,并以此作为大扰动临界切除时刻的依据,将此刻的系统状态变量输入训练好的人工神经网络即可输出CUEP。通过IEEE-39节点系统证明了该方法的正确性与有效性,并且较传统的牛顿法具有一定的优势与实用价值。
提出了一种基于DNSPPSO算法的船舶综合电网暂态电压失稳后的快速重构方法。该方法将船舶综合电力系统受到大扰动后的紧急控制问题转化为船舶电网重构问题,并基于船舶电网安全性及实时性要求提出了一种快速的新型粒子群优化算法。该算法融合了小种群技术与动态邻域技术：小种群技术可显著减小种群规模,并经过一定迭代次数后,在保留全局最优位置与适应度值的基础上重新生成全部粒子；采用动态邻域法克服了以往权重法处理多目标优化问题的缺陷,可根据船舶综合电力系统的特定任务直接输出最终的计算结果,无需人为从Pareto解集中挑选最终解。数值仿真采用某8节点船舶电网实例并对比SPPSO与DPSO算法,证明了该方法的有效性。
As the development and application of electric science technology, the capacity of Shipboard Power System (SPS) is continuing to be larger. All kinds of drive, monitor control and protect equipments have realized electrical automation, demanding more and more power. Meanwhile, the capacity of single load has also become larger. As a result, voltage drops of SPS caused by impact load connecting often occur. Thus, the research of transient voltage stability for SPS has become more and more important. Based on the summarization of previous work, this dissertation conducts systematical research work on several key aspects of transient voltage stability under large disturbance of SPS.
Characters of scenarios on losing transient voltage stability of SPS under large disturbance are analyzed. Furthermore, it is concluded that the research on transient voltage stability of SPS is very special. Based on the characters and analyses, math model is built for SPS. Then, numerical simulation on transient voltage stability under large disturbance is executed by utilizing time domain analysis in transient stability analysis in electric power system. Finally, by using a SPS example, simulation demonstrates math model and numerical integration method correct. It is concluded that this method could be basic and validating methodology for further research of transient voltage stability.
Time varying character analysis method for judging SPS on the transient voltage stable edge or not is proposed. This method reviews the main conclusions of character analysis in small disturbance analysis. Then, system status equations are expended according to Taylor series at each epoch. After that, liberalized Jacobian expression is deduced at the epoch. Meanwhile, small disturbance analysis for the linear equations is executed. By calculating all the eigen values and left and ri ght eigen vectors and dynamic participating factors of eigen vectors corresponding with all status variables, theory criterion for judging SPS on the transient voltage stable edge or not is concluded. Finally, by utilizing a SPS simulation example, the effectiveness of the methodology is proved.
A methodology of calculating Controlling Unstable Equilibrium Point (CUEP) on SPS transient voltage stability boundary based on BP neural network is presented. First, high dimensions non-linear mapping relationship between status variables at different fault cut times and at stable state is built according to electric power system stability boundary theory. Second, transient stability numerical simulations under different large disturbance cut time are executed for times to record data of status variables at different fault cut times and at stable state. Data of status variables at different fault cut times is regarded as input sample and data of status variables at stable state is regarded as output sample, respectively. Furthermore, all the data is used to train BP neural network. Third, the algorithm results CUEP by inputting variables at critical cut time, which is calculated by the intersection point of fault trajectory and stable manifold into the trained BP neural network. Finally, the methodology is demonstrated correct and effective by IEEE39nodes system. Meanwhile, the novel method is more advanced and practical compared with traditional Newton Method.
A fast reconfiguration method based on DNSPPSO algorithm after losing trainsient voltage stability for SPS is proposed. The methodology converts emergency control problem after large disturbance of SPS into a shipboard power system reconfiguration problem. Furthermore, a fast and novel particle swarm optimization algorithm is presented based on the security and real-time demand of SPS. This algorithm combines small population technology and dynamic neighborhood technology together. Small population technology could reduce population size greatly. Besides, the technique regenerate all the particles with retaining global and personal best finesses and positions within the search space every few iterations. At the same time, dynamic neighborhood technique can overcome the shortcomings of weight method effectively when handling multi-objectives optimization problem. Meanwhile, dynamic neighborhood technique could result the final solution directly according to specified mission without pick is out from Pareto front. Numerical simulation is run through an8-bus SPS example to demonstrate the effectiveness of this algorithm by comparing with SPPSO and DPSO algorithm.
分析了船舶综合电力系统及大负荷冲击下的暂态电压失稳场景的特点,指出船舶综合电力系统暂态电压稳定性的研究具有其特殊性。然后基于上述特点对船舶综合电力系统进行了数学建模,运用电力系统暂态稳定分析中的时域仿真法对数学模型进行大扰动下的暂态电压稳定数值仿真。以某船舶综合电力系统为仿真算例证明了数学模型及数值积分方法的正确性。该内容可作为进一步研究暂态电压稳定性的基本方法与验证手段。
提出了一种判断船舶综合电力系统是否处于暂态电压失稳边界的时变特征分析方法。该方法首先回顾了小扰动分析中特征分析法的主要结论,结合船舶综合电力系统暂态过渡过程的综合数学模型,提出在每一时步将系统状态方程组按泰勒级数展开,获得在该时步的线性化Jacobian表达式并进行小扰动分析；通过求解出全部特征根、左右特征向量及定义重要状态变量与之对应特征向量的动态相关因子,给出船舶综合电力系统是否处于暂态电压失稳状态的理论判据。通过某船舶综合电力系统的仿真算例进行了验证,证明了上述方法的有效性。
提出了一种基于BP神经网络的船舶综合电网暂态电压稳定边界上的CUEP计算方法。该方法在电力系统稳定域边界理论基础上,建立系统受扰后各切除时刻状态及达到稳态后系统状态间的高维非线性映射关系；通过多次不同大扰动切除时刻的暂态稳定数值仿真,记录下多组受扰后各切除时刻状态变量及达到稳态后系统状态变量,将其分别作为输入与输出样本训练BP神经网络；估算持续故障轨迹与该故障模式主导下稳定流形的交界点,并以此作为大扰动临界切除时刻的依据,将此刻的系统状态变量输入训练好的人工神经网络即可输出CUEP。通过IEEE-39节点系统证明了该方法的正确性与有效性,并且较传统的牛顿法具有一定的优势与实用价值。
提出了一种基于DNSPPSO算法的船舶综合电网暂态电压失稳后的快速重构方法。该方法将船舶综合电力系统受到大扰动后的紧急控制问题转化为船舶电网重构问题,并基于船舶电网安全性及实时性要求提出了一种快速的新型粒子群优化算法。该算法融合了小种群技术与动态邻域技术：小种群技术可显著减小种群规模,并经过一定迭代次数后,在保留全局最优位置与适应度值的基础上重新生成全部粒子；采用动态邻域法克服了以往权重法处理多目标优化问题的缺陷,可根据船舶综合电力系统的特定任务直接输出最终的计算结果,无需人为从Pareto解集中挑选最终解。数值仿真采用某8节点船舶电网实例并对比SPPSO与DPSO算法,证明了该方法的有效性。
As the development and application of electric science technology, the capacity of Shipboard Power System (SPS) is continuing to be larger. All kinds of drive, monitor control and protect equipments have realized electrical automation, demanding more and more power. Meanwhile, the capacity of single load has also become larger. As a result, voltage drops of SPS caused by impact load connecting often occur. Thus, the research of transient voltage stability for SPS has become more and more important. Based on the summarization of previous work, this dissertation conducts systematical research work on several key aspects of transient voltage stability under large disturbance of SPS.
Characters of scenarios on losing transient voltage stability of SPS under large disturbance are analyzed. Furthermore, it is concluded that the research on transient voltage stability of SPS is very special. Based on the characters and analyses, math model is built for SPS. Then, numerical simulation on transient voltage stability under large disturbance is executed by utilizing time domain analysis in transient stability analysis in electric power system. Finally, by using a SPS example, simulation demonstrates math model and numerical integration method correct. It is concluded that this method could be basic and validating methodology for further research of transient voltage stability.
Time varying character analysis method for judging SPS on the transient voltage stable edge or not is proposed. This method reviews the main conclusions of character analysis in small disturbance analysis. Then, system status equations are expended according to Taylor series at each epoch. After that, liberalized Jacobian expression is deduced at the epoch. Meanwhile, small disturbance analysis for the linear equations is executed. By calculating all the eigen values and left and ri ght eigen vectors and dynamic participating factors of eigen vectors corresponding with all status variables, theory criterion for judging SPS on the transient voltage stable edge or not is concluded. Finally, by utilizing a SPS simulation example, the effectiveness of the methodology is proved.
A methodology of calculating Controlling Unstable Equilibrium Point (CUEP) on SPS transient voltage stability boundary based on BP neural network is presented. First, high dimensions non-linear mapping relationship between status variables at different fault cut times and at stable state is built according to electric power system stability boundary theory. Second, transient stability numerical simulations under different large disturbance cut time are executed for times to record data of status variables at different fault cut times and at stable state. Data of status variables at different fault cut times is regarded as input sample and data of status variables at stable state is regarded as output sample, respectively. Furthermore, all the data is used to train BP neural network. Third, the algorithm results CUEP by inputting variables at critical cut time, which is calculated by the intersection point of fault trajectory and stable manifold into the trained BP neural network. Finally, the methodology is demonstrated correct and effective by IEEE39nodes system. Meanwhile, the novel method is more advanced and practical compared with traditional Newton Method.
A fast reconfiguration method based on DNSPPSO algorithm after losing trainsient voltage stability for SPS is proposed. The methodology converts emergency control problem after large disturbance of SPS into a shipboard power system reconfiguration problem. Furthermore, a fast and novel particle swarm optimization algorithm is presented based on the security and real-time demand of SPS. This algorithm combines small population technology and dynamic neighborhood technology together. Small population technology could reduce population size greatly. Besides, the technique regenerate all the particles with retaining global and personal best finesses and positions within the search space every few iterations. At the same time, dynamic neighborhood technique can overcome the shortcomings of weight method effectively when handling multi-objectives optimization problem. Meanwhile, dynamic neighborhood technique could result the final solution directly according to specified mission without pick is out from Pareto front. Numerical simulation is run through an8-bus SPS example to demonstrate the effectiveness of this algorithm by comparing with SPPSO and DPSO algorithm.
引文
[1]Emadi A. Modeling, analysis, and stability assessment of multi-converter power electronic systems:[dissertation]. Tehran:Sharif University of Technology,2000.
[2]Xie Y H. Modeling, analysis, and control of DC hybrid power systems:[dissertation]. Michigan:The University of Michigan,2010.
[3]Altosole M, Benvenuto G, Figari M, et al. Real-time simulation of a COGAG naval ship propulsion system. Proceedings of the Institution of Mechanical Engineers, Part M:Journal of Engineering for the Maritime Environment,2009,223:47-61.
[4]Qi L. AC system stability analysis and assessment for shipboard power systems: [dissertation]. Texas:Texas A & M University,2004.
[5]Ciezki G J, Ashton W R. Selection and stability issues associated with a navy shipboard DC zonal electric distribution system. IEEE Transactions on Power Delivery, 2000,15(2):665-669.
[6]Huang K, Cartes D A, Srivastava S K. A multiagnet-based algorithm for ring-structured shipboard power system reconfiguration. IEEE Transactions on System, Man, and Cybernetics-Part C:Applications and Review,2007,37(5):1016-1021.
[7]Deshmukh A A. Agent based control and monitoring system dynamic reallocation of loads in a limited power supply scenario:[dissertation]. South Carolina:University of South Carolina,2006.
[8]Jiang Z H. Agent-based power sharing scheme for active hybrid power sources. Journal of Power Sources,2008,177(177):231-238.
[9]Ding Z P, Srivastava K S. Dynamic simulation-based analysis of a new load shedding scheme for a notional destroyer-class shipboard power system. IEEE Transactions on Industry Applications,2009,45(3):1166-1174.
[10]Srivastava S, Butler-burry K L. Expert-system method for automatic reconfiguration for restoration of shipboard power systems. IEEE Proceedings of Generation, Transmission and Distribution,2006,153(3):253-260.
[11]Huang K, Srivastava K S, Cartes A D, et al. Market-based multiagent system for reconfiguration of shipboard power systems. Electric Power Systems Research,2009, 79(79):550-556.
[12]Solanki M J. Multi-agent based control and reconfiguration for restoration of distribution systems with distributed generators:[dissertation]. Mississippi: Mississippi State University,2006.
[13]Yu Q L, Schulz N N, Srivastava K A. Multi-agent based reconfiguration of AC-DC shipboard distribution power system. Integrated Computer-Aided Engineering,2007, 17(17):347-357.
[14]Momoh A J, Zhu J Z, Kaddah S S. Optimal load shedding study of naval-ship power system using the Everett optimization technique. Electric Power Systems Research, 2002,60(60):145-152.
[15]Srivastava S, Butler-burry K L. Probability-based predictive self-healing reconfiguration for shipboard power systems. IET Generation, Transmission & Distribution,2007,1(3):405-413.
[16]Padamati R K. Reconfiguration of shipboard power systems using a genetic algorithm:[dissertation]. Mississippi:Mississippi State University,2007.
[17]Butler-Purry L K, Sarma R D N. self-healing reconfiguration for restoration of naval shipboard power system. IEEE Transactions on Power Systems,2004,19(2): 754-762.
[18]Seenumani G, Peng H, Sun J. A reference governor-based hierarchical control for failure mode power management of hybrid power systems for all-electric ships. Journal of Power Sources,2011,196(196):1599-1607.
[19]Mitra P, Venayagamoorthy K G. An adaptive control strategy for DSTATCOM applications in an electric ship power system. IEEE Transactions on Power Electronics, 2010,25(1):95-104.
[20]Kaddah S S. Cost effective damage control analysis for shipboard power system: [dissertation]. Washington, D.C.:Howard University,2002.
[21]Chan R R, Lee Y, Sudhoff D S, et al. Evolutionary optimization of power electronics based power systems. IEEE Transactions on Power Electronics,2008,23(4):1907-1917.
[22]Paul D, Haddadian V. Transient overvoltage protection of shore-to-ship power supply system. IEEE Transactions on Industry Applications,2011,47(3):1193-1200.
[23]张全来.船舶电力推进监控系统研究：[硕士学位论文].武汉：华中科技大学,2007.
[24]许友.船舶电力系统暂态稳定性的研究：[硕士学位论文].哈尔滨：哈尔滨工程大学,2008.
[25]赵云利.船舶综合全电力推进系统的建模与计算机仿真研究：[硕士学位论文].哈尔滨： 哈尔滨工程大学,2006.
[26]杨琳.电力推进系统对船舶电网稳定的影响研究：[硕士学位论文].哈尔滨：哈尔滨工程大学,2010.
[27]孙才勤.船舶电力系统建模仿真及动态稳定性研究：[博士学位论文].大连：大连海事大学,2010.
[28]薛士龙.船舶电力推进仿真装置及其关键技术的研究：[博士学位论文].上海：上海海事大学,2006.
[29]高海波.船舶电力推进系统的建模与仿真：[博士学位论文].武汉：武汉理工大学,2008.
[30]张腾飞.粗糙集理论及粗糙混合智能方法在船舶电力系统中的应用研究：[博士学位论文].上海：上海海事大学,2007.
[31]施伟锋.关于船舶电力系统研究的一些探索：[博士学位论文].上海：上海海事大学,2005.
[32]Amjady N. A framework of reliability assessment with consideration effect of transient and voltage stability. IEEE Transactions on Power Systems,2004,19(2): 1005-1014.
[33]Esmaili M, Adjady N, Shayanfar A H. Stochastic multi-objective congestion management in power markets improving voltage and transient stability. European Transactions on Electrical Power,2011,21(21):99-115.
[34]Kenne G, Goma R, Nkwawo H, et al. An improved direct feedback linearization technique for transient stability enhancement and voltage regulation of power generators. Electrical Power and Energy Systems,2010,32(32):809-816.
[35]Cong L, Wang Y, Hill D J. Transient stability and voltage regulation enhancement via coordinated control of generator excitation and SVC,2005,27(27):121-130.
[36]段献忠,何仰赞,陈德树.电力系统暂态电压稳定的基本概念和仿真分析.华中理工大学学报,1995,23(4)：21-24.
[37]徐泰山,薛禹胜,韩祯祥.暂态电压稳定的模型要求和快速判断.电力系统自动化,1995,19(12)：11-15.
[38]王义红,梅生伟.基于稳定裕度指标的暂态电压稳定分析.电工电能新技术,2007,26(2)：39-44.
[39]袁志昌,刘文华,宋强.基于暂态电压稳定指标的动态无功优化配置方法.电力系统自动化,2009,33(14)：17-21.
[40]李立理,陆超,黄志刚.计及感应电动机的负荷节点暂态电压稳定解析评估方法.电力系统自动化,2009,33(7)：1-5.
[41]林舜江,李欣然,刘杨华,等.考虑负荷动态模型的暂态电压稳定快速判断方法.中国电机工程学报,2009,29(4)：14-20.
[42]井艳清,李兴源,郭晓鸣,等.考虑感应电动机负荷模型的暂态电压稳定快速判据.电力系统自动化,2011,35(5)：10-14.
[43]刘斌,陈磊,闵勇.考虑暂态电压稳定的低压减载地点选择.电力系统自动化,2008,32(5)：11-14.
[44]赵珊珊,仲悟之,张东霞,等.暂态电压稳定风险评估方法及应用.电力系统自动化,2009,33(19)：1-4.
[45]吴浩,郭瑞鹏,韩祯祥.电力系统微分代数模型的奇异性和暂态电压稳定.电力系统自动化,2006,30(13)：16-21.
[46]林舜江,刘杨华.简单电力系统暂态电压稳定的直接分析法.湖南工业大学学报,2011,25(1)：70-76.
[47]刘文华,宋强,滕乐天,等.暂态电压稳定与新型动态无功补偿装置.电工技术学报,2007,22(7)：18-23.
[48]管秀鹏.南方电网暂态电压稳定分析研究：[博士学位论文],北京：清华大学,2004.
[49]倪以信,陈寿孙,张宝霖.动态电力系统的理论和分析.北京：清华大学出版社,2002.
[50]仲悟之.受端系统暂态电压稳定机理研究：[博士学位论文],北京：中国电力科学研究院,2008.
[51]Fouad A A, Stanton S E. Transient stability of a multimachine power system Part Ⅰ and Part Ⅱ. IEEE Transactions on Power Apparatus and System,1981,100(7): 3408-3424.
[52]Chiang H D, Wu F F, Varaiya P P. Foundation of the potential energy boundary surface method for power system transient stability analysis. IEEE Transactions on Circuits and Systems,1988,35(6):712-728.
[53]Xue Y, Van C T, Ribbens-Pavella M. Extended equal area criterion justifications, generations, applications. IEEE Transactions on Power Systems,1989,4(1):44-52.
[54]王义红,张雪敏,孙玉娇,等.含感应电动机的独立电力系统暂态电压分析.清华大学学报(自然科学版),2011,51(1)：36-42.
[55]周双喜,朱凌志,等.电力系统电压稳定性及其控制.北京：中国电力出版社,2004.
[56]刘雨.船舶柴油发电机组建模和智能控制的仿真研究：[硕士学位论文].大连：大连海事大学,2002.
[57]Liu Y C, Wang C. Modeling and simulation on ship power station under large disturbance. Advanced Materials Research,2010,121-122(121-122):74-79.
[58]王锡凡,方万良,杜正春.现代电力系统分析.北京：科学出版社,2003.
[59]管霖,王同文,唐宗顺.电网安全监测的智能化关键特征识别及稳定分区算法.电力系统自动化.2006,30(21)：22-27.
[60]Fouad A A, Stanton S E. Transient stability of a multimachine power system Part I and Part II. IEEE Transactions on Power Apparatus and System,1981,100(7): 3408-3424.
[61]Chiang H D, Wu F F, Varaiya P P. Foundation of the potential energy boundary surface method for power system transient stability analysis. IEEE Transactions on Circuits and Systems,1988,35(6):712-728.
[62]Xue Y, Van C T, Ribbens-Pavella M. Extended equal area criterion justifications, generations, applications. IEEE Transactions on Power Systems,1989,4(1):44-52.
[63]Rovnyak S, Kretsinger S, Thorp J, et al. Decision trees for real-time transient stability prediction. IEEE Transactions on Power Systems,1994,9(3):1417-1426.
[64]Jurado F, Saenz R J. Detecting transient voltage stability and voltage sag. International Journal for Computation and Mathematics in Electrical and Electronic Engineering,2004,23(2):392-409.
[65]Shubhanga N K, Kulkarni M A. Determination of effectiveness of transient stability controls using reduced number of trajectory sensitivity computations. IEEE Transactions on Power Systems,2004,19(1):473-482.
[66]Ruiz-Reyes N, Vera-Candeas P, Jurado F. Discrimination between transient voltage stability and voltage sag using damped sinusoids-based transient modeling. IEEE Transactions on Power Delivery,2005,20(4):2644-2650.
[67]Esmaili M, Shayanfar A H, Amjady N. Multi-objective congestion management incorporating voltage and transient stabilities. Energy,2009,34(34):1401-1412.
[68]Wiszniewski A. New criteria of voltage stability margin for the purpose of load shedding. IEEE Transactions on Power Delivery,2007,22(3):1367-1371.
[69]Chen H, Wang Y, Zhou R. Transient and voltage stability enhancement via coordinated excitation and UPFC control. IEE Proceeding of Generation, Transmission, Distribution,2001,148(3):201-208.
[70]Kato Y, Iwamoto S. Transient stability preventive control for stable operating condition with desired CCT. IEEE Transactions on Power Systems,2002,17(4): 1154-1161.
[71]Vera-Candeas P, Ruiz-Reyes N, Jurado F. Transient voltage stability and voltage sag discrimination by matching pursuit-based transient modeling and neural networks. Electric Power Components and Systems,2006,34(3):321-341.
[72]Ayasun S, Nwankpa O C, Kwatny G H. Voltage stability toolbox for power systems education and research. IEEE Transactions on Education,2006,49(4):432-442.
[73]孙元章,张剑云.电力系统暂态安全的观测与控制.电力系统及其自动化学报.2007,19(1)：9-13.
[74]马世英,印永华,汤涌,等.短期和中长期电压稳定仿真及评价.电网技术.2006,30(19)：14-20.
[75]谢欢,张保会,沈宇,等.基于WAMS的电力系统暂态紧急控制启动方案.电网技术,2009,33(20)：59-64.
[76]秦晓辉,毕天姝,杨奇逊,等.基于WAMS动态轨迹的电力系统功角失稳判据.电力系统自动化,2008,32(23)：18-22.
[77]谢欢,张保会,李钢,等.基于广域发电机状态信息的电力系统暂态不稳定性实时预测.电力自动化设备,2009,29(7)：28-32.
[78]谢欢,张保会,于广亮,等.基于轨迹几何特征的暂态不稳定识别.中国电机工程学报,2008,28(4)：16-22.
[79]叶圣永.基于机器学习的电力系统暂态稳定评估研究：[博士学位论文].成都：西南交通大学,2010.
[80]蔡国伟,穆钢,Chan W K,等.基于网络信息的暂态稳定性定量分析—支路势能法.中国电机工程学报,2004,24(5)：1-6.
[81]刘彦呈,王川,魏一.电力系统大扰动下暂态电压稳定的时变特征分析.电力系统保护与控制,2011,39(10)：11-17.
[82]赵茜茜,王晓茹.电力系统扰动后稳态频率预测快速算法.电力系统保护与控制,2011,39(1)：72-78.
[83]Chiang H D, Wu F F, Varaiya P P. Foundations of PEBS method for power systems transient stability analysis. IEEE Trans on Circuits and Systems,1988,35(6):86-115.
[84]傅书逖.势能界面法(PEBS)暂态稳定分析的综述及展望.电力系统自动化,1998, 22(9)：16-18.
[85]蒋健,刘明波.基于BUC法的多故障暂态稳定约速最优潮流计算.电力系统保护与控制,2008,36(23)：8-14.
[86]赵庆生,刘笙,陈陈.持续故障轨线位能边界曲面出口识别方法研究.继电器,2005,33(4)：6-9.
[87]Chiang H D, Wu F F, Varaiya P P. A BCU method for direct analysis of power system stability[J]. IEEE Trans on Power Systems,1994,9(3):1194-1208.
[88]薛禹胜.DEEAC的理论证明—四论暂态能量函数直接法.电力系统自动化,1993,17(7)：7-19.
[89]Maria G A, Tang C, Kim J. Hybrid transient stability analysis. IEEE Trans on Power Systems,1990,5(2):1431-1438.
[90]林济铿,关磊,罗萍萍,等.求解Controlling-UEP新方法的研究.电力系统自动化,2005,29(23)：25-28.
[91]刘彦呈,王川,魏一.采用BP神经网络求解CUEP的实用计算.电力系统保护与控制,2011,39(22)：144-149.
[92]刘笙,汪静.电力系统暂态稳定的能量函数分析：[硕士学位论文].上海：上海交通大学出版社,1996.
[93]林舜江,刘明波.暂态电压失稳模型的主导不稳定平衡点计算.华南理工大学学报：自然科学版,2009,37(11)：88-94.
[94]李颖晖,张保会.运用非线性系统理论确定电力系统暂态稳定域的一种新方法.中国电机工程学报,2000,20(1)：41-44.
[95]王成山,贾宏杰,江晓东.一种寻找正确相关不稳定平衡点的基准方法.天津大学学报：1999,32(5)：529-534.
[96]李颖晖,张保会,徐成贤.应用开关算法计算电力系统中的主导不稳定平衡点.西安交通大学学报,2003,37(4)：407-410.
[97]王晓阳,闵勇,侯凯元.改进伴随系统法求解电力系统主导不稳定平衡点.电力系统自动化,2006,30(23)：7-10.
[98]王开鹏,张毅威,闵勇.基于伴随系统理论的发电机复杂模型下CUEP计算.清华大学学报：自然科学版,2009,49(11)：1866-1870.
[99]廖晓昕.动力系统的稳定性理论和应用.北京：国防工业出版社,2000.
[100]张芷芬,丁同仁,黄文社,等.微分方程定性理论.北京：科学出版社,1997.
[101]滕林.电力系统暂态稳定在线决策算法的研究[博士学位论文].北京：华北电力大学 2003.
[102]史真惠,朱守真,郑竞宏,等.改进BP神经网络在负荷动静比例确定中的应用.中国电机工程学报,2004,24(7)：25-29.
[103]周双喜,朱凌志,郭锡玖,等.电力系统电压稳定性及其控制.北京：中国电力出版社,2004.
[104]Ahuja A, Das S, Pahwa A. An AIS-ACO hybrid approach for multi-objective distribution system reconfiguration. IEEE Transactions on Power Systems,2007,22(3): 1101-1111.
[105]Gomes F V, Carneiro Jr. Sandoval Pereira, Vinagre M P, et al. A new heuristic reconfiguration algorithm for large distribution systems. IEEE Transactions on Power Systems,2005,20(3):1373-1378.
[106]Jin X, Zhao J, Sun Y, et al. Distribution network reconfiguration for load balancing using binary particle swarm optimization. Proceedings of International Conference on Power System Technology, Singapore,2004:507-510.
[107]Chiou J P, Chang C F, Su C T. Variable scaling hybrid differential evolution for solving network reconfiguration of distribution systems. IEEE Transactions on Power Systems,2005,20(2):668-674.
[108]Ahuja A, Pahwa A. Using ant colony optimization for loss minimization in distribution networks. Proceedings of 37th North American Power Symposium. Ames, IA, United states,2005:470-474.
[109]Carpaneto E, Chicco G. Distribution system minimum loss reconfiguration in the hyper-cube ant colony optimization framework. Electric Power System Research,2008, 78(12):2037-2045.
[110]Mitra P, Venayagamoorthy G K. Implementation of an intelligent reconfiguration algorithm for an electric ship's power system. IEEE Transactions on Industry Applications,2011,47(5):2292-2300.
[111]Ding Z, Srivastava S K, Cartes D A, et al. Dynamic simulation-based analysis of a new load shedding scheme for a notional destroyer-class shipboard power system. IEEE Transactions on Industry Applications,2009,45(3):1166-1174.
[112]Kumar N, Srivastava A, Schulz N N. Shipboard power systems restoration using binary particle swarm optimization. Proceedings of 39th North American Power Symposium, Las Cruces, NM, United states,2007:164-169.
[113]Padamati K R, Schulz N N, Srivastava A K. Application of genetic algorithm for reconfiguration of shipboard power system. Proceedings of 39th North American Power Symposium, Las Cruces, NM, United states,2007:159-163.
[114]Yu Q L, Schulz N N, Srivastava A K. Multi-agent based reconfiguration of AC-DC shipboard distribution power system. Integrated Computer-Aided Engineering,2010, 17(4):347-357.
[115]Solanki J M, Solanki S K, Schulz N N. Multi-agent-based reconfiguration for restoration of distribution systems with distributed generators. Integrated Computer-Aided Engineering,2010,17(4):331-346.
[116]Hu X H, Eberhart R. Multiobjective optimization using dynamic neighborhood particle swarm optimization. Proceedings of the 2002 Congress on Evolutionary Computation, Honolulu, Hawaii, United states,2002:1677-1681.
[117]Das T K, Venayagamoorthy G K, Aliyu U 0. Bio-inspired algorithms for the design of multiple optimal power system stabilizaer:SPPSO and BFA. IEEE Transactions on Industry Applications,2008,44(5):1445-1457.
[118]Abido M A. A niched Pareto genetic algorithm for multi-objective environment/economic dispatch. International Journal of Electrical Power & Energy Systems,2003,25(2):97-105.
[119]Alrashii M R, E1-Hawary M E. Pareto fronts of the emission economic dispatch under different loading conditions. International Journal of Electrical and Electronics Engineering,2008,2(10):596-599.
[120]Kennedy J, Eberhart R. Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, United States,1995: 1942-1948.
[121]Iwasaki N, Yasuda K, Ueno G. Dynamic parameter tuning of particle swarm optimization. International Journal of Electrical and Electronics Engineering,2006, 2006(1):353-363.
[122]Kennedy J, Eberhart R C. Discrete binary version of the particle swarm algorithm. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Orlando, FL, United states,1997:4104-4108.
[2]Xie Y H. Modeling, analysis, and control of DC hybrid power systems:[dissertation]. Michigan:The University of Michigan,2010.
[3]Altosole M, Benvenuto G, Figari M, et al. Real-time simulation of a COGAG naval ship propulsion system. Proceedings of the Institution of Mechanical Engineers, Part M:Journal of Engineering for the Maritime Environment,2009,223:47-61.
[4]Qi L. AC system stability analysis and assessment for shipboard power systems: [dissertation]. Texas:Texas A & M University,2004.
[5]Ciezki G J, Ashton W R. Selection and stability issues associated with a navy shipboard DC zonal electric distribution system. IEEE Transactions on Power Delivery, 2000,15(2):665-669.
[6]Huang K, Cartes D A, Srivastava S K. A multiagnet-based algorithm for ring-structured shipboard power system reconfiguration. IEEE Transactions on System, Man, and Cybernetics-Part C:Applications and Review,2007,37(5):1016-1021.
[7]Deshmukh A A. Agent based control and monitoring system dynamic reallocation of loads in a limited power supply scenario:[dissertation]. South Carolina:University of South Carolina,2006.
[8]Jiang Z H. Agent-based power sharing scheme for active hybrid power sources. Journal of Power Sources,2008,177(177):231-238.
[9]Ding Z P, Srivastava K S. Dynamic simulation-based analysis of a new load shedding scheme for a notional destroyer-class shipboard power system. IEEE Transactions on Industry Applications,2009,45(3):1166-1174.
[10]Srivastava S, Butler-burry K L. Expert-system method for automatic reconfiguration for restoration of shipboard power systems. IEEE Proceedings of Generation, Transmission and Distribution,2006,153(3):253-260.
[11]Huang K, Srivastava K S, Cartes A D, et al. Market-based multiagent system for reconfiguration of shipboard power systems. Electric Power Systems Research,2009, 79(79):550-556.
[12]Solanki M J. Multi-agent based control and reconfiguration for restoration of distribution systems with distributed generators:[dissertation]. Mississippi: Mississippi State University,2006.
[13]Yu Q L, Schulz N N, Srivastava K A. Multi-agent based reconfiguration of AC-DC shipboard distribution power system. Integrated Computer-Aided Engineering,2007, 17(17):347-357.
[14]Momoh A J, Zhu J Z, Kaddah S S. Optimal load shedding study of naval-ship power system using the Everett optimization technique. Electric Power Systems Research, 2002,60(60):145-152.
[15]Srivastava S, Butler-burry K L. Probability-based predictive self-healing reconfiguration for shipboard power systems. IET Generation, Transmission & Distribution,2007,1(3):405-413.
[16]Padamati R K. Reconfiguration of shipboard power systems using a genetic algorithm:[dissertation]. Mississippi:Mississippi State University,2007.
[17]Butler-Purry L K, Sarma R D N. self-healing reconfiguration for restoration of naval shipboard power system. IEEE Transactions on Power Systems,2004,19(2): 754-762.
[18]Seenumani G, Peng H, Sun J. A reference governor-based hierarchical control for failure mode power management of hybrid power systems for all-electric ships. Journal of Power Sources,2011,196(196):1599-1607.
[19]Mitra P, Venayagamoorthy K G. An adaptive control strategy for DSTATCOM applications in an electric ship power system. IEEE Transactions on Power Electronics, 2010,25(1):95-104.
[20]Kaddah S S. Cost effective damage control analysis for shipboard power system: [dissertation]. Washington, D.C.:Howard University,2002.
[21]Chan R R, Lee Y, Sudhoff D S, et al. Evolutionary optimization of power electronics based power systems. IEEE Transactions on Power Electronics,2008,23(4):1907-1917.
[22]Paul D, Haddadian V. Transient overvoltage protection of shore-to-ship power supply system. IEEE Transactions on Industry Applications,2011,47(3):1193-1200.
[23]张全来.船舶电力推进监控系统研究：[硕士学位论文].武汉：华中科技大学,2007.
[24]许友.船舶电力系统暂态稳定性的研究：[硕士学位论文].哈尔滨：哈尔滨工程大学,2008.
[25]赵云利.船舶综合全电力推进系统的建模与计算机仿真研究：[硕士学位论文].哈尔滨： 哈尔滨工程大学,2006.
[26]杨琳.电力推进系统对船舶电网稳定的影响研究：[硕士学位论文].哈尔滨：哈尔滨工程大学,2010.
[27]孙才勤.船舶电力系统建模仿真及动态稳定性研究：[博士学位论文].大连：大连海事大学,2010.
[28]薛士龙.船舶电力推进仿真装置及其关键技术的研究：[博士学位论文].上海：上海海事大学,2006.
[29]高海波.船舶电力推进系统的建模与仿真：[博士学位论文].武汉：武汉理工大学,2008.
[30]张腾飞.粗糙集理论及粗糙混合智能方法在船舶电力系统中的应用研究：[博士学位论文].上海：上海海事大学,2007.
[31]施伟锋.关于船舶电力系统研究的一些探索：[博士学位论文].上海：上海海事大学,2005.
[32]Amjady N. A framework of reliability assessment with consideration effect of transient and voltage stability. IEEE Transactions on Power Systems,2004,19(2): 1005-1014.
[33]Esmaili M, Adjady N, Shayanfar A H. Stochastic multi-objective congestion management in power markets improving voltage and transient stability. European Transactions on Electrical Power,2011,21(21):99-115.
[34]Kenne G, Goma R, Nkwawo H, et al. An improved direct feedback linearization technique for transient stability enhancement and voltage regulation of power generators. Electrical Power and Energy Systems,2010,32(32):809-816.
[35]Cong L, Wang Y, Hill D J. Transient stability and voltage regulation enhancement via coordinated control of generator excitation and SVC,2005,27(27):121-130.
[36]段献忠,何仰赞,陈德树.电力系统暂态电压稳定的基本概念和仿真分析.华中理工大学学报,1995,23(4)：21-24.
[37]徐泰山,薛禹胜,韩祯祥.暂态电压稳定的模型要求和快速判断.电力系统自动化,1995,19(12)：11-15.
[38]王义红,梅生伟.基于稳定裕度指标的暂态电压稳定分析.电工电能新技术,2007,26(2)：39-44.
[39]袁志昌,刘文华,宋强.基于暂态电压稳定指标的动态无功优化配置方法.电力系统自动化,2009,33(14)：17-21.
[40]李立理,陆超,黄志刚.计及感应电动机的负荷节点暂态电压稳定解析评估方法.电力系统自动化,2009,33(7)：1-5.
[41]林舜江,李欣然,刘杨华,等.考虑负荷动态模型的暂态电压稳定快速判断方法.中国电机工程学报,2009,29(4)：14-20.
[42]井艳清,李兴源,郭晓鸣,等.考虑感应电动机负荷模型的暂态电压稳定快速判据.电力系统自动化,2011,35(5)：10-14.
[43]刘斌,陈磊,闵勇.考虑暂态电压稳定的低压减载地点选择.电力系统自动化,2008,32(5)：11-14.
[44]赵珊珊,仲悟之,张东霞,等.暂态电压稳定风险评估方法及应用.电力系统自动化,2009,33(19)：1-4.
[45]吴浩,郭瑞鹏,韩祯祥.电力系统微分代数模型的奇异性和暂态电压稳定.电力系统自动化,2006,30(13)：16-21.
[46]林舜江,刘杨华.简单电力系统暂态电压稳定的直接分析法.湖南工业大学学报,2011,25(1)：70-76.
[47]刘文华,宋强,滕乐天,等.暂态电压稳定与新型动态无功补偿装置.电工技术学报,2007,22(7)：18-23.
[48]管秀鹏.南方电网暂态电压稳定分析研究：[博士学位论文],北京：清华大学,2004.
[49]倪以信,陈寿孙,张宝霖.动态电力系统的理论和分析.北京：清华大学出版社,2002.
[50]仲悟之.受端系统暂态电压稳定机理研究：[博士学位论文],北京：中国电力科学研究院,2008.
[51]Fouad A A, Stanton S E. Transient stability of a multimachine power system Part Ⅰ and Part Ⅱ. IEEE Transactions on Power Apparatus and System,1981,100(7): 3408-3424.
[52]Chiang H D, Wu F F, Varaiya P P. Foundation of the potential energy boundary surface method for power system transient stability analysis. IEEE Transactions on Circuits and Systems,1988,35(6):712-728.
[53]Xue Y, Van C T, Ribbens-Pavella M. Extended equal area criterion justifications, generations, applications. IEEE Transactions on Power Systems,1989,4(1):44-52.
[54]王义红,张雪敏,孙玉娇,等.含感应电动机的独立电力系统暂态电压分析.清华大学学报(自然科学版),2011,51(1)：36-42.
[55]周双喜,朱凌志,等.电力系统电压稳定性及其控制.北京：中国电力出版社,2004.
[56]刘雨.船舶柴油发电机组建模和智能控制的仿真研究：[硕士学位论文].大连：大连海事大学,2002.
[57]Liu Y C, Wang C. Modeling and simulation on ship power station under large disturbance. Advanced Materials Research,2010,121-122(121-122):74-79.
[58]王锡凡,方万良,杜正春.现代电力系统分析.北京：科学出版社,2003.
[59]管霖,王同文,唐宗顺.电网安全监测的智能化关键特征识别及稳定分区算法.电力系统自动化.2006,30(21)：22-27.
[60]Fouad A A, Stanton S E. Transient stability of a multimachine power system Part I and Part II. IEEE Transactions on Power Apparatus and System,1981,100(7): 3408-3424.
[61]Chiang H D, Wu F F, Varaiya P P. Foundation of the potential energy boundary surface method for power system transient stability analysis. IEEE Transactions on Circuits and Systems,1988,35(6):712-728.
[62]Xue Y, Van C T, Ribbens-Pavella M. Extended equal area criterion justifications, generations, applications. IEEE Transactions on Power Systems,1989,4(1):44-52.
[63]Rovnyak S, Kretsinger S, Thorp J, et al. Decision trees for real-time transient stability prediction. IEEE Transactions on Power Systems,1994,9(3):1417-1426.
[64]Jurado F, Saenz R J. Detecting transient voltage stability and voltage sag. International Journal for Computation and Mathematics in Electrical and Electronic Engineering,2004,23(2):392-409.
[65]Shubhanga N K, Kulkarni M A. Determination of effectiveness of transient stability controls using reduced number of trajectory sensitivity computations. IEEE Transactions on Power Systems,2004,19(1):473-482.
[66]Ruiz-Reyes N, Vera-Candeas P, Jurado F. Discrimination between transient voltage stability and voltage sag using damped sinusoids-based transient modeling. IEEE Transactions on Power Delivery,2005,20(4):2644-2650.
[67]Esmaili M, Shayanfar A H, Amjady N. Multi-objective congestion management incorporating voltage and transient stabilities. Energy,2009,34(34):1401-1412.
[68]Wiszniewski A. New criteria of voltage stability margin for the purpose of load shedding. IEEE Transactions on Power Delivery,2007,22(3):1367-1371.
[69]Chen H, Wang Y, Zhou R. Transient and voltage stability enhancement via coordinated excitation and UPFC control. IEE Proceeding of Generation, Transmission, Distribution,2001,148(3):201-208.
[70]Kato Y, Iwamoto S. Transient stability preventive control for stable operating condition with desired CCT. IEEE Transactions on Power Systems,2002,17(4): 1154-1161.
[71]Vera-Candeas P, Ruiz-Reyes N, Jurado F. Transient voltage stability and voltage sag discrimination by matching pursuit-based transient modeling and neural networks. Electric Power Components and Systems,2006,34(3):321-341.
[72]Ayasun S, Nwankpa O C, Kwatny G H. Voltage stability toolbox for power systems education and research. IEEE Transactions on Education,2006,49(4):432-442.
[73]孙元章,张剑云.电力系统暂态安全的观测与控制.电力系统及其自动化学报.2007,19(1)：9-13.
[74]马世英,印永华,汤涌,等.短期和中长期电压稳定仿真及评价.电网技术.2006,30(19)：14-20.
[75]谢欢,张保会,沈宇,等.基于WAMS的电力系统暂态紧急控制启动方案.电网技术,2009,33(20)：59-64.
[76]秦晓辉,毕天姝,杨奇逊,等.基于WAMS动态轨迹的电力系统功角失稳判据.电力系统自动化,2008,32(23)：18-22.
[77]谢欢,张保会,李钢,等.基于广域发电机状态信息的电力系统暂态不稳定性实时预测.电力自动化设备,2009,29(7)：28-32.
[78]谢欢,张保会,于广亮,等.基于轨迹几何特征的暂态不稳定识别.中国电机工程学报,2008,28(4)：16-22.
[79]叶圣永.基于机器学习的电力系统暂态稳定评估研究：[博士学位论文].成都：西南交通大学,2010.
[80]蔡国伟,穆钢,Chan W K,等.基于网络信息的暂态稳定性定量分析—支路势能法.中国电机工程学报,2004,24(5)：1-6.
[81]刘彦呈,王川,魏一.电力系统大扰动下暂态电压稳定的时变特征分析.电力系统保护与控制,2011,39(10)：11-17.
[82]赵茜茜,王晓茹.电力系统扰动后稳态频率预测快速算法.电力系统保护与控制,2011,39(1)：72-78.
[83]Chiang H D, Wu F F, Varaiya P P. Foundations of PEBS method for power systems transient stability analysis. IEEE Trans on Circuits and Systems,1988,35(6):86-115.
[84]傅书逖.势能界面法(PEBS)暂态稳定分析的综述及展望.电力系统自动化,1998, 22(9)：16-18.
[85]蒋健,刘明波.基于BUC法的多故障暂态稳定约速最优潮流计算.电力系统保护与控制,2008,36(23)：8-14.
[86]赵庆生,刘笙,陈陈.持续故障轨线位能边界曲面出口识别方法研究.继电器,2005,33(4)：6-9.
[87]Chiang H D, Wu F F, Varaiya P P. A BCU method for direct analysis of power system stability[J]. IEEE Trans on Power Systems,1994,9(3):1194-1208.
[88]薛禹胜.DEEAC的理论证明—四论暂态能量函数直接法.电力系统自动化,1993,17(7)：7-19.
[89]Maria G A, Tang C, Kim J. Hybrid transient stability analysis. IEEE Trans on Power Systems,1990,5(2):1431-1438.
[90]林济铿,关磊,罗萍萍,等.求解Controlling-UEP新方法的研究.电力系统自动化,2005,29(23)：25-28.
[91]刘彦呈,王川,魏一.采用BP神经网络求解CUEP的实用计算.电力系统保护与控制,2011,39(22)：144-149.
[92]刘笙,汪静.电力系统暂态稳定的能量函数分析：[硕士学位论文].上海：上海交通大学出版社,1996.
[93]林舜江,刘明波.暂态电压失稳模型的主导不稳定平衡点计算.华南理工大学学报：自然科学版,2009,37(11)：88-94.
[94]李颖晖,张保会.运用非线性系统理论确定电力系统暂态稳定域的一种新方法.中国电机工程学报,2000,20(1)：41-44.
[95]王成山,贾宏杰,江晓东.一种寻找正确相关不稳定平衡点的基准方法.天津大学学报：1999,32(5)：529-534.
[96]李颖晖,张保会,徐成贤.应用开关算法计算电力系统中的主导不稳定平衡点.西安交通大学学报,2003,37(4)：407-410.
[97]王晓阳,闵勇,侯凯元.改进伴随系统法求解电力系统主导不稳定平衡点.电力系统自动化,2006,30(23)：7-10.
[98]王开鹏,张毅威,闵勇.基于伴随系统理论的发电机复杂模型下CUEP计算.清华大学学报：自然科学版,2009,49(11)：1866-1870.
[99]廖晓昕.动力系统的稳定性理论和应用.北京：国防工业出版社,2000.
[100]张芷芬,丁同仁,黄文社,等.微分方程定性理论.北京：科学出版社,1997.
[101]滕林.电力系统暂态稳定在线决策算法的研究[博士学位论文].北京：华北电力大学 2003.
[102]史真惠,朱守真,郑竞宏,等.改进BP神经网络在负荷动静比例确定中的应用.中国电机工程学报,2004,24(7)：25-29.
[103]周双喜,朱凌志,郭锡玖,等.电力系统电压稳定性及其控制.北京：中国电力出版社,2004.
[104]Ahuja A, Das S, Pahwa A. An AIS-ACO hybrid approach for multi-objective distribution system reconfiguration. IEEE Transactions on Power Systems,2007,22(3): 1101-1111.
[105]Gomes F V, Carneiro Jr. Sandoval Pereira, Vinagre M P, et al. A new heuristic reconfiguration algorithm for large distribution systems. IEEE Transactions on Power Systems,2005,20(3):1373-1378.
[106]Jin X, Zhao J, Sun Y, et al. Distribution network reconfiguration for load balancing using binary particle swarm optimization. Proceedings of International Conference on Power System Technology, Singapore,2004:507-510.
[107]Chiou J P, Chang C F, Su C T. Variable scaling hybrid differential evolution for solving network reconfiguration of distribution systems. IEEE Transactions on Power Systems,2005,20(2):668-674.
[108]Ahuja A, Pahwa A. Using ant colony optimization for loss minimization in distribution networks. Proceedings of 37th North American Power Symposium. Ames, IA, United states,2005:470-474.
[109]Carpaneto E, Chicco G. Distribution system minimum loss reconfiguration in the hyper-cube ant colony optimization framework. Electric Power System Research,2008, 78(12):2037-2045.
[110]Mitra P, Venayagamoorthy G K. Implementation of an intelligent reconfiguration algorithm for an electric ship's power system. IEEE Transactions on Industry Applications,2011,47(5):2292-2300.
[111]Ding Z, Srivastava S K, Cartes D A, et al. Dynamic simulation-based analysis of a new load shedding scheme for a notional destroyer-class shipboard power system. IEEE Transactions on Industry Applications,2009,45(3):1166-1174.
[112]Kumar N, Srivastava A, Schulz N N. Shipboard power systems restoration using binary particle swarm optimization. Proceedings of 39th North American Power Symposium, Las Cruces, NM, United states,2007:164-169.
[113]Padamati K R, Schulz N N, Srivastava A K. Application of genetic algorithm for reconfiguration of shipboard power system. Proceedings of 39th North American Power Symposium, Las Cruces, NM, United states,2007:159-163.
[114]Yu Q L, Schulz N N, Srivastava A K. Multi-agent based reconfiguration of AC-DC shipboard distribution power system. Integrated Computer-Aided Engineering,2010, 17(4):347-357.
[115]Solanki J M, Solanki S K, Schulz N N. Multi-agent-based reconfiguration for restoration of distribution systems with distributed generators. Integrated Computer-Aided Engineering,2010,17(4):331-346.
[116]Hu X H, Eberhart R. Multiobjective optimization using dynamic neighborhood particle swarm optimization. Proceedings of the 2002 Congress on Evolutionary Computation, Honolulu, Hawaii, United states,2002:1677-1681.
[117]Das T K, Venayagamoorthy G K, Aliyu U 0. Bio-inspired algorithms for the design of multiple optimal power system stabilizaer:SPPSO and BFA. IEEE Transactions on Industry Applications,2008,44(5):1445-1457.
[118]Abido M A. A niched Pareto genetic algorithm for multi-objective environment/economic dispatch. International Journal of Electrical Power & Energy Systems,2003,25(2):97-105.
[119]Alrashii M R, E1-Hawary M E. Pareto fronts of the emission economic dispatch under different loading conditions. International Journal of Electrical and Electronics Engineering,2008,2(10):596-599.
[120]Kennedy J, Eberhart R. Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, United States,1995: 1942-1948.
[121]Iwasaki N, Yasuda K, Ueno G. Dynamic parameter tuning of particle swarm optimization. International Journal of Electrical and Electronics Engineering,2006, 2006(1):353-363.
[122]Kennedy J, Eberhart R C. Discrete binary version of the particle swarm algorithm. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Orlando, FL, United states,1997:4104-4108.
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